[Auto-generated transcript. Edits may have been applied for clarity.] Welcome. Um, this is a guest lecture for a module on the Master's in Translational Health Sciences,
which is run jointly through continuing education and the Department of Primary Care Health Sciences.
My name is Trish Greenhouse.
I'm program director, and I want to extend a really warm welcome to not just to our own master students, most of whom are in the room here,
but also lots of people who've joined online and, uh, also some who are in the room who know that master student.
So I think we've got a hugely diverse group here.
And now I'm going to introduce Carolyn, and I'm so pleased you're here.
Um, you are on visiting fellowship at the this institute in Cambridge, which is a it's run by Mary Dixon Woods.
And they do wacky things with implementation science, pushing back on some of the assumptions.
And I hear from Karen, you have the most amazing interdisciplinary background,
which started with an undergraduate joint harnessing chi busy ology, looking up if you don't know that it's fun and sociology.
And then you went on to do various management t organisationally things.
Um, which equipped you for hanging out with the Institute of Health Policy Management and Evaluation at the fabulous University of Toronto.
Um, but the the reason why I'm so excited is, you know, this Barry is an enablers thing.
What am I say to you guys at the beginning of the week? You give me barriers in the neighbourhoods, you might pass you.
You challenge those barriers and enablers framing. You probably get a distinction.
Well, you're going to tell us how it's done. Thank you.
Thank you. And I understand it's going to be one of those back and forth sessions where there's going to be lots of interactivity.
Uh, questions are going to pop up. I think you are okay with them.
Put your hands in the all right stuff. I'm just going to go and sit in the corner much.
I think. I think I'm on camera now for the online people. Okay, I'll try and stand here.
Novel wandered. Thanks so much and so happy to be here.
Um, really. Thank you. Uh, to Caroline and Trish for the invitation to chat with the module.
I had a chance to read a little bit more about what you guys have been learning all week.
And, uh, what an exciting opportunity, um, for for you guys and for, um, for me to be able to engage with you in what you've been learning this week.
Um, so Esther says I'm from the University of Toronto, in addition to my associate professor role and senior investigating role,
I hold what's called a Canada research chair in implementing digital health innovation.
So in Canada, we have these kind of nationally recognised chairs where we get to present an issue we think really matters,
and the government gets to decide if we get to go and do it or not.
And so I'm one of those and I pitch that implementation matters a lot and digital health matters.
And that's what I've been working on the last five years.
And uh, we'll be renewing that for, for the next five, uh, because it's a journey that continues.
And one of the reasons I really like implementation science, um,
is because it kind of helps us with this problem that you guys have probably talked a lot about,
like that notorious 17 years gap between discovery and actual adoption.
And it's a really sticky one is this paper was out in 2023 from Jama.
But yeah, and I seen this title in a lot of papers over a lot of years, and it just doesn't seem to be budging very much.
Um, and I'm going to talk a little bit about how we might think about using implementation science theory in particular,
and theories that underpin implementation science to maybe help shrink this a
little bit more than they've been able to do over the last couple of decades.
So I'm already going to put you guys to work. I'm gonna put you to work a couple of times.
Maybe you have a piece of paper in front of you. Maybe you have your phone, your laptop.
Um, I'd like us to think a little bit about what we've actually been measuring when we're doing change.
So on your piece of paper or your note on the phone, even better, find a new piece of paper.
I'll show you. Why. Because drawing pictures is a fun part of this work.
I want to think of you guys to think about maybe a reform or an innovation that you've been a part of.
Maybe you've studied it as a researcher, or maybe you've implemented it as an organisational leader.
Maybe you designed it as an innovator. Um, and think about what, um, your experience of doing that was.
So what you're going to do was on the left side of your piece of paper or on one part of your phone,
you're going to write down what the innovation is or what current in 2020 likes to call the thing.
What is your thing, your innovation, the implementation that they were going to put in place?
What's your thing? And on the right side, what did you hope the thing was going to do for you?
What was your outcome that you were trying to get to? So have a thought on that.
And then in the middle, write down all the things you kind of cared about or measure.
So if you're researching it or if you were trying to drive it, what were your KPIs, your key performance indicators?
What were the things you kind of measured between the thing and the outcome?
I'll give you just like a couple of minutes to think about that. But I take a sense of this lovely coffee that was.
Like you to take a look at your list. Um, did anybody write down explicitly something about how you went from the thing to the outcome?
Yeah. And we often do that. Right? What's the thing that we're doing? What were the outcomes that we measured.
And implementation folks will do that as well. But there's all this stuff that's happening in the middle, right, that we know what the thing is.
And then we measure this outcome and then we think, okay, now we we're going to make it work.
And then we try to hand it to people. And then it doesn't really take the way we think it should.
And that's where what you guys have been learning about should be able to help with that.
And in fact, when we go back to that, um, paper from 2023, it suggests to us that the burgeoning field of implementation science written in 2023,
I feel like it was a couple, at least a couple of decades old at that point, but that should be helping us out.
But not all. Implementation science is kind of the same and may or may not be helping us on that journey.
So we'll talk about that a bit today. So hold on to your piece of paper.
Think about your thing and your outcome, and let's start working on filling in the middle of it.
Um, but what often happens for us as we really notice that there's determinants in the literature, we really like determinants.
We did a really interesting, um, one study led by, uh, Genevieve Rouleau and uh, in published just in 2024,
where we looked at just studies, uh, digitally implementation of digital health.
And we wanted to look at the theories, models and frameworks that were being used in those studies over a 20 year period.
Uh, we looked found 156 studies that were published in that time.
68 different theories, models and frameworks were used.
The vast majority of determinants, frameworks, and really surprisingly,
about 20% of those determinants frameworks were just cipher a lots of consolidated framework for implementation research that's getting used.
We can talk a lot about why that's the case. But overall the field in digital health and others really love determinant studies.
We love it. Last year uh for a talk I want you know,
for for fun I wanted to see in just 2025 how many studies of determinants in digital health were out there and there were over 100.
This is not a systematic review. This is just me playing around with Google Scholar.
But that's an awful lot of papers on just determinants. So as a field we just really love digital.
Um, and I really encourage to hear that you guys were talk to you about this at the beginning, that maybe we need to move beyond,
um, because there's a concern growing amongst implementation scientists and other people that as the science has matured,
uh, it's arguably becoming structurally comfortable that we're really good at cataloguing determinants and strategies,
but we're really under theorising mechanisms, which is really the key thing that happens between the thing and the outcome.
Um, and as I wrote in 2023 with, uh, their colleagues that theoretical products at describing what have generally outpaced explanations of how in
implementation science and as evidenced by those numerous determinant frameworks but fewer explanatory theories.
So we saw that in our scoping review and others who are also working in the field
doing similar scoping reviews in other like outside of just digital health,
are overwhelmingly seeing this to be the case. So it's really important to remember that determinants matter.
They're important for for some key things, but they're not really explanations all the time.
Strategies that we also publish aren't necessarily causal processes.
And outcomes don't necessarily mean we've understood what happened.
Right. We see this in trials all the time. We we saw an outcome and that's awesome.
And then we try to pick the thing up and put it to another place. And we can't for the life of us figure out why it didn't happen in the other place.
And that's all the middle part between our thing and our outcome.
So mechanistic thinking or mechanisms are a way forward in this.
And that's and this is not a brand new space. A lot of implementation scientists and other theorists have been grappling with mechanisms.
Realist thinking has been grappling with mechanisms for a long time.
Um, so I'm going to spend today talking about how we can think differently about mechanisms, um,
the different theoretical foundations that help us uncover where they are and some of the challenges
we might face if we're really going in and trying to understand this component of implementation.
So there are lots of different definitions and understandings, and generally not an one agreed upon, um, definition that is being used.
Our realist friends will suggest mechanisms are kind of this, uh,
resources and reasoning components that trigger that are triggered within a particular context to lead to an outcome.
Those are the context mechanism, outcomes groupings that you tend to see.
Um, del can explain expanded that to suggest mechanisms exist in resources and reasoning, um, as well.
So we can kind of think about them in that way. Uh, Lewis Carroll, Lewis and a group of colleagues out of the states have been working in,
in mechanisms for quite some time, and they've been trying to catalogue them to the best of their ability for a long time.
And they define them as processes through which implementation strategies exert effects.
Um, so very much focussed on the implementation component, how you connect your strategy for doing the thing to lead to the outcome you're expecting.
And then, uh, more on the theoretic kind of theoretical, sociological, middle range theory spaces.
We have folks like Romain and Tracy Finch doing normalisation process theory thinking, where they suggest mechanisms,
or these generative social processes, uh, that explain how a practice becomes normalised, at least in the the example of that particular theory.
But there are other middle range theories that effectively say that same thing.
But there's a causal thing that's happening under the surface that's leading from the thing to the outcome or the action.
But generally, they're all agreeing that mechanisms are about explaining how and why a change happens.
Um, the how and why being critical determines sort of telling you what, not necessarily how or why they're functioning.
So another way I like to share this idea with people is using a light switch analogy.
I used to use a trail running analogy, which only worked for a very small pocket of people who knew what trail running.
So I was strongly encouraged to not use that analogy.
So now I'm on a light switch and a light bulb, which makes a lot more sense.
So if you standing in this lovely room and we would like to light up the room a little bit.
So I see some lights here and I go over to the wall and I turn the light switch on and off the light went on.
Magic. However, for that light to turn on because that light switch was switched, there's all this stuff happening in the walls of the building.
There's all the stuff happening in the environment around us to create power that goes to that light switch,
to make sure that that light went on, because that light switch was turned on and not a fan was turned on or not.
A light in another room was turned on. And the key with mechanisms is they tend to exist in places we don't always obviously see them.
We're kind of in the walls. They're kind of in our minds.
They're kind of in our social norms that exist in our environments around us.
So they're not readily available for measurement, and they're not readily available for us to see or understand them,
which is part of why we don't go in that direction when we go to evaluate or measure or understand what we see,
because it's not necessarily something we can't see. So they matter because we might have asked them this time.
It's a large ocean liner, and here's our thing chugging along and we're like, this is definitely going to work.
But then what happens is we face a resistance iceberg. And often at the top of that iceberg are all those determinants that we can see and measure.
But what the mechanisms that are important are the ones that actually underpin the things that we see and measure.
They don't exist differently from the mechanisms. They're actually kind of part of why or differently from the determinants.
They actually are part of why the determinants are the determinants in the first place.
And they rest on they're influenced by these mechanisms that are things like affect and beliefs and norms and values that lead to things like,
um, general resistance or workflow problems or resource allocation issues.
There are underlying pieces about why the things we see at the surface are actually happening at all.
So we need a way to kind of dive deeper into what the determinants are in order to understand why they matter to us in the first place.
And that's where our action probably needs to happen.
Um, that's why when you go and put something in practice and you fix all your work flow in one place, you're in a great fixed work flow.
And then I go do it over here. And that work flow fix just isn't working.
It's because something else is happening underneath the surface that you need to uncover.
Um, now, there's also some new exciting studies that have shot that has shown, particularly this one from Williams and company,
that mechanisms can have actual like causal explanations for implementation success.
This one came out just a month ago, which I'm super psyched about is in all my talks over the next few months.
Um, and certainly Carl May and Tracy Vance. You design normalisation process theory are also super pumped about this.
Uh, because they ran, uh, they did a secondary analysis of data out of a hybrid type three trial of it implementing a digital, um, tool.
This was a on clinician decision support tool or. Sorry, this was a, uh, tool used in mental health and outpatient clinics for youth.
Uh, they were able to use the Nomad's tool, which measures mechanisms of coherence and collective action and cognitive participation,
all the mechanisms underlying normalisation process theory.
And they were able to see, like causal reasons for implementation working when those mechanisms were turned on,
when that like dependent outcome occurred. Um, which was very exciting.
Um, and then they saw that there was a statement of the implementation over a six month period when those mechanisms were turned on.
So not only important when you're putting anything in place, but maybe also important to sustain the new thing over time.
So really encouraging data coming out. So that's one way of doing finding out what your mechanisms are.
You can go out and actually study them and figure out causal pathways. But uh, has done kind of suggests there are lots of other ways we can find, um,
mechanisms in the world that we can look for things like strong association, which happened with the Williams paper.
You can dive in and do some specificity work or consistency work,
which is actually getting into being able to quantify what a mechanism is and tested empirically over time.
Um, you can do experimental implementations of experimental manipulation or look at things occurring over time and look for patterns over time.
Um, all of these things are quite complex and require a good amount of measurement so that you can one understand the mechanism is there,
and to be able to measure it consistently over time.
And because those things are hard to do when you're trying to measure something like social norms often land on the plausibility or of or coherence,
um, requirement of identifying mechanisms.
So Carol Lewis in that group, um, back in 2020, had done a review of the literature to see how mechanisms were being unveiled in the literature.
And overwhelmingly plausibility. Coherence was the main way that we were uncovering it, because we were still really in a qualitative space,
working in smaller environments and not being able to do these bigger replication pieces of work.
So the Williams paper is very encouraging and shows us ways that we can embed mechanism,
mechanistic measurement into bigger trials with bigger numbers,
and start getting into those higher order measurement opportunities that cast and suggests for
us what I will say is what I'm going to present next as we go through some other mechanisms.
Um, tends to rest on plausibility or coherence, um, as a way to understand that these are mechanisms available to us,
because that's kind of a lot of the data that we have available to us. So I think it's still conversational and still things that need to be tested.
Um, I'm going to talk about why theory helps us when in the plausibility or coherence space.
Another way. We've uncovered this in some of the work that we've done,
and that's one of these really fun piece of work I'm doing with colleague, uh, Jay Shaw in, uh, University of Toronto.
And I think a number of people here all know Jay was like, hey, do you know Jay?
Yeah. Uh, so we're trying to uncover, um, how to change practice for community health workers, um, and understand what their needs are.
And we're doing a bunch of co-design work in terms of understanding their roles, uh, in community health work in different environments across Canada.
So we have sites in Ontario where I'm from. We have sites in B.C., um, as well, where Nancy Clark is leading that part of the work.
And so we were trying to understand what they were facing in terms of the challenges of doing their work as community health workers.
And in order for us to dive deeper into understanding root causes or mechanisms leading
to those barriers and enablers that they were experiencing as community health workers,
we just kept asking why? So this is an image from one of our mural boards that we used in our co-production work.
So we'd say, okay, what's the challenge? You're experiencing a mental health.
You say, oh, why do you think that is? And explains this? Okay, well why do you think that is?
And then they explain to us again.
And so they only see two layers of this, but it just keeps going and going and going until you get down to I can't say why anymore.
And that's where you kind of get to like values and norms and beliefs when there isn't really another why.
It's just what's deeply rooted in people, in systems, in our environments.
Um, I hear you heard lurkers love a complexity theory.
So think about all those factors and complexity theory, then into those root reasons why things are happening or not happening.
And that's when some of that, like magical mechanistic stuff starts happening.
Um, and that's where you can start uncovering it and understanding it, at least from a coherence and plausibility, um, perspective.
I'm going to make you go to work again. Go back to your pieces of paper or your phones.
You have your thing on the left. You have your outcome on the right.
We've talked a little bit about what the middle could be.
If you wrote some things down in the middle already, stuff you measured, can you think about that a little deeper?
If you saw something on the surface that occurred that maybe got in the way or helped what
might be underlying that thing that got in the way or helped your thing to its outcome?
And I got to a something with you.
Um, I'm curious if anyone wants to share with us what you were thinking about, or possibly how hard that might have been.
Um, the risk one is a really interesting one, right? That, um, if you presented a new idea and it feels less risky,
then you're kind of driving down into individual kind of behaviour mechanism that our tolerance for risk really shapes how we behave or not behave.
Um, excellent example of how you can think about that. Moving into mechanistic where we're in can are great questions to ask.
Like where is the mechanism. I'm going to present some stuff that's going to make that answer really hard to figure out.
Um, but uh, I think the piece you, you clicked in on the midwife, introducing something gets you there, right?
The interest. So where you would start is the midwife introduce something that's like the action we did.
And then you got to ask, well, why does that work? Why is that helping a person actually use a technology?
And there's lots of really interesting literature coming out around how we can transfer
trust from an individual into trust to a technology that if someone presents one to us,
that if we have a trusting, good relationship with them, that's transferred into the technology space.
And it also comes from, um, trust transfer in teams.
So that's in the team's literature for, um, like a long time where people can work within a team,
even if it's a new person, because they trust the team and you could potentially transfer them to technology.
Some newer papers coming out on that. Very cool.
So as you start getting into the next level of why that will help you.
I'm going to show you some things about, um, some theory that can help you.
So I did a little bit of a deep dive with our example here about how we can try to use theory to inform understanding why a mechanism works or not.
And, you know, Tristan, a nice job of talking about my very multi-disciplinary background.
There's political science in there, too.
So I had, you know, 4 or 5 different disciplinary backgrounds, and I think that's probably why I like implementation science,
because implementation science is like the science for people who don't want to pick things,
because it requires understanding cognitive and social psychology.
We need to understand why people do things and don't do things. You need to understand sociology.
You need to understand why societies shift or move, or in anthropology,
you have to understand why cultures shift or move, and how culture sharing groups behave.
And all of these disciplinary traditions have been around for a long time, have been doing the work of understanding change.
Right. We don't need to reinvent a lot of that. And some of this is like deeply, empirically based.
Um, they show it in labs, they show it consistently in real world.
Why people, teams, organisations, policies, systems and cultures change over time.
We don't have to reinvent this. There are excellent theories we can go to.
And it's those why questions that these theories have been grappling with that help us understand mechanisms.
Those are the causal links that we can go to and actually test and look for in our implementation work.
So I'm a big fan of going back to disciplinary traditions. I teach a graduate course kind of on this around how you use this to pick the right theory,
model or framework for a particular challenge by recognising what is your big lever of change in the implementation you're doing?
Sometimes we know, sometimes we don't. Um, in the example of kind of pharmacists taking on new, um, uh, new prescribing behaviours.
There's like a couple of big levers we can think about, right?
There's political levers, which you noted, uh,
being able to share that role is another big lever that being clinicians reading power to another clinician.
That's a big lever in each of those levers kind of hook into these different kinds of traditions of understanding change.
And that's how you would go ahead and pick a theory to help you understand the challenge change that's
happening and something you can actually test and use and maybe appropriately rebuilding over time.
So I'm going to go through a few of them and we'll go through all of them.
I'll go through a few that we've been able to look at and get some coherence level understanding of mechanisms and,
and some of the work that we've done. And hopefully this sparked some, some ideas in the example you've been working on, um,
for mechanisms that might be underlying the stuff that you're seeing or in any projects that you have coming up.
So let's start with cognitive mechanisms. It's often when we think about brain because decisions are made by humans.
Um, and these can be things like habit formation, mental models, identity from an individual standpoint.
We'll talk about identity from a global standpoint. Um, and there are different theoretical approaches that try to understand cognitive behaviour,
things that we use in implementation science or things like they can be or they capability,
opportunity, motivation, behaviour and related theory or plan behaviour and um behaviour change.
We'll a lot of those behaviour change theories.
And if you go into their like foundational papers and how they were written first they'll tell you the assumptions that they're built on.
They'll tell you the causal mechanisms that they're resting on.
And that really helps you understand why they believe these things are important to a shift in behaviour over time.
I often encourage people try to find, like the first paper that talked about the theory,
because they don't always carry the assumptions of the theory forward as they continue to write about it.
And this becomes really important for us to understand the assumptions that are underpinning our thinking.
Um, so one example I am very happy to share.
And it's a little this one's a little sad. Um, like I actually passed just two months ago very tragically.
Uh, and this is one of the projects we got to do together. Micah was a brilliant clinician, um, family doc, and worked in the ICU at our hospital.
And he'd been working in rural, remote parts of Ontario for a long time, and he really believed in GLP ones for diabetic patients.
He'd seen so much evidence for their effectiveness. He saw that they were helping his his patients.
He was deep into the literature, but his colleagues weren't using it and they weren't prescribing it.
Certainly in the North, where diabetes was a significant problem for the patient population of their,
particularly our First Nations and indigenous populations.
And so he came to me, he's like, you know, I know you do a lot of co-design work. Let's co-design a new training platform.
They do this nice digital training platform and that's going to solve this problem.
And I said, are you sure it's a training problem? Like is it that they don't know what it is and they don't know how to prescribe it?
And then he kind of thought about that and said, no, well, maybe, maybe it's not.
So we took a step back and used some funding.
He got through one of his many career awards that he was getting at the time to do a deep dive.
It was qualitative work and did some interviews with the clinicians that in his environment,
and we applied calmly to it to understand what was shifting or not shifting their prescribing behaviour.
And some really interesting things came out of that. Things like their clinical identity,
who they understood themselves to be and what their job was as a physician was changing whether they were prescribing things or not.
Um, their beliefs in GLP one and the social construction around Ozempic as a concept was shifting their behaviour,
whether there was clinical evidence or not, that social influence was really important and meaningful to them,
their past experiences and their risk assessments, all of those things mattered a lot.
And if we'd gone ahead and just built a new technology enabled training platform, sure,
it had accessibility to them, but it wouldn't have hit on these really key things.
And what that meant was different kinds of relationship building, different kinds of sense making.
And although we won't get to do it with Mike, we will try and carry this forward to, to, um,
to do some interventions with those clinicians so we can kind of see his vision forward.
So those are some cognitive mechanisms. There are lots though. You can think about lots of behaviour change stuff.
Let's talk a little bit about sociological mechanisms.
These are a little trickier because they exist both within individuals and within our social systems I think for complexity conversation.
Right. All of these things are like a big blanket of interwoven fibres where our individual
behaviour is shifted and shaped and moved by our social systems and fabrics.
Right. That that individual clinician isn't necessarily just working as an individual clinician in a vacuum.
There's all these forces acting on us. Things like norms, things like power networks and then identity from a shared sense,
things about not just who am I as a person, but who am I as a physician?
Who am I as a parent? Who am I as a child?
All of these kind of simple constructions within shared groups that shape our behaviour as well.
So identity is a sticky one that lands in a couple of places.
And then the theoretical approaches we want at this level are a little different.
They're not necessarily about individual cognition. They're about like how the individual interacts with their social environment.
So then you get into like some classic social interactions in theory.
I'm always a big fan of Goffman and he's like front stage backstage kind of stuff.
I think that really resonates well. You can get into critical theory and think about power and, um,
kind of structures of power environments and how that shifts our ability to move or not.
Um, and then socialisation processes, if you're a fan of Foucault, this one is always really fun.
And if you can think a lot about Inter in terms of professional identity creation, that as people go through the training of their profession,
they're socialised into that identity and that shifts their behaviour and what they think is the right or wrong way of doing things,
and it also shifts the environments around them. So mechanisms that might emerge from this level might be things like identity and normative threat.
So what is threatening my power as a clinician?
What might be threatening my role as a, um, a caregiver?
Um, there might also be legitimacy construction. So what is a legitimate or illegitimate form of action within what is understood within my society?
Um, and there's also things like network contagion that always sounds kind of fun,
but it's basically that when something happens in one part of a network, it can trickle and move towards other parts of a close knit network.
Um, we've looked at this a little bit in the shared identity space of professional identity.
Um, and this is work by my lovely colleague, uh, Jennifer Schuldiner.
Um, in 2021. And then we did some work in 2024. Um,
all the years leading into it that found in virtual care environments that things like professional identity were really important to use or not use,
and for virtual care. So doctors, nurses work, looked at emergency virtual emergency departments.
Uh, during Covid they were setting these up all over Canada, definitely in Toronto and in her hospital in Toronto.
And they were finding some clinicians loved it and some clinicians hated it.
And they were trying to find out why.
And they found that the clinicians who really hated it said things like, this isn't what it means to be an emergency room doctor.
And then we did some work and social representation and virtual care and primary care.
Did some deep ethnographic work, talk to patients, talk to their caregivers, talked to providers.
After watching an interaction in a virtual environment. And lots of clinicians used it and lots of clinicians didn't.
And the ones who didn't use it said, this is not what it means to be a family doctor.
This is not how I was trained. This isn't how I understand what my role is.
And so virtual care was a very immediate and real threat to what it meant to them to be a good doctor,
which for them was being in a room with another person. But that wasn't everybody.
There were lots of clinicians in the study for whom it resonated strongly with their identity as a clinician,
and it meant that they could do a better job of connecting, particularly some of the social workers and um,
um, nursing staff who were doing much more kind of mental health and social support pieces,
and probably because the patients were more vulnerable in a virtual environment than they could be in person.
So that allowed them to tap into their personal identity of being a good social worker that could
connect with somebody emotionally because there was now availability to connect to them emotionally.
And a lot of that was wrapped in the construction of how they were trained and what their expectations were over time.
So those are very different kinds of mechanisms we think about. I'll talk about how that changes strategy in just a moment.
There's also organisational and team mechanisms so important when we think about these ones.
Yes, the individuals still acting, but individuals can also act on behalf of their team or on behalf of their organisation.
So for example, when the leaders or directors are acting, they're not necessarily making decisions because of what they, as an individual want to.
They're making it a kind of, um, for their organisation,
and they're kind of embedding what their organisation wants to do and what their priorities are,
and all of those things that they can, you know, constructed in. Um, so that's how they're acting.
So we need a different kind of theoretical lens to understand that kind of change that's happening, things we can look at.
Now back to normalisation process theory, which talks of teams changing behaviour over time and and broadly team science,
lots of organisational behaviour theory like ecology and institutionalisation, lots of things that talk about why an organisation changes or not.
Tons of little literature there and then you can get into some shared mental model stuff.
So mental models can like how an individual understands the world.
Um, when we share that amongst a group, that becomes something within teams or organisations, um, that you can work with,
mechanisms we might see here are things like collective sense making, role renegotiation, trust,
collaboration, things that are kind of happening at a team interactional, larger group level.
Um, I actually ended up adding this slide this morning because I forgot the study was that, um,
and this was actually done by a postdoc of mine, and she moves so quickly into her scientist's role, I forgot how fun this was.
And this actually came out last year where we did a realist synthesis to understand context mechanism outcomes for learning, um, organisations.
She wants to go and figure out what makes a learning health system a learning health system,
and what are the mechanisms that drive that learning health system to exist.
Well, so she's building on this work, but this one came out, um, just last year.
And this is our foundational work. There's Marissa there.
Um, and where we wound up finding, you know, 60 different CMO groupings across the literature,
but we were able to distil them down into six big areas.
Um, and they kind of look like this as an example.
So this is one of our six. So embracing risk and failure.
Uh, a CMO in this space looks like when evaluation is promoted as a learning activity within an organisation.
So learning health systems or learning learning organisations, um, that tolerates risk and does not shame and blame staff for evaluation failures.
That's for context. Uh, staff involved in the evaluation are more apt to engage with and learn from that evaluation.
There's your outcome. And because they do not fear your mechanism, disciplinary punitive measures associated with evaluation failure.
So we have our context, our environment of the outcome we're trying to get to.
And then our mechanism in the middle. So realist thinking really does help us unpack what the mechanism is.
Uh, we were able to distil the 16 into these six things.
And she's in the process of testing, um, on a now, what is really fun about this paper, um,
that I reread and Marissa and I talked about so much is that each of those six kind of big buckets are really at this underlying level.
That first one mechanism that's about fear, that's second one.
If you go and read the paper, it's going to be all about provenance. That third one's about a sense of ownership and values.
The fourth one is about meaningfulness of data.
I love this one because we've been doing this in digital health for a long time, and meaning and data is so crucial.
That fourth one, the fifth one is about motivation and the last one is about kind of contributed role.
So we're back to like a shared role and share identity in that last one.
So all six of them are operating at this lower level.
Yeah. But yeah just a quick question.
Um, so I guess several, but um, I'm looking at the learning system and I'm wondering where does the data come from?
And with regards to the evaluation piece, has there been any conversations about, um,
what forms that takes into or is, is is sort of, um, there specific teams that sort of do that?
Is it. Everybody does it together. Is individuals having that reflective spirit within, uh, team.
I'm really curious for that part of that question.
Just to clarify, are you asking about like, data that informed building this framework or like data used in a learning health system?
No, no, the data in the learning health system, in the learning health.
And so, um, Marissa and that team over actually in health partners, they've been doing their entire system is learning health systems.
So they have a really nice framework that talks about different sources of data.
Um, it can it helps when if it's coming from something the organisation's already collecting anyway.
Um, because trying to give them new stuff to do is very hard.
But what you end up doing is shifting, um, their understanding of the data and presenting it in a different way in order to adapt these things.
So, for example, you can get to a sense of ownership and value if you sit down with the clinicians.
Who you'd be asking to do something different and say, how do you think about this data?
What do you think we should be collecting? How should it be presented to you?
And suddenly they're part of the conversation. It's making sense to them.
So you're going to hook into the data is meaningful piece. And then they feel like it's not something that's been thrust upon them.
It's something they actually went and asked for. Um, so often I doing it that way, it's helpful.
Um, my other wonderful colleague, Harry Carlucci, does a ton in patient experience, and she took the patient experience data they were doing already.
She tweeted to improve it. And Hannah has now expanded that into caregiver experience.
And she worked with clinicians and worked with her decision and the kind of key decision makers to present data in real time,
back to clinicians on tablets. And it just exists in their environment.
And because they're kind of competitive people, they like to see how they're doing and they like to see how their neighbours are doing.
So it's presented unity unit, but they also extracted really meaningful, um, quotes from the experience surveys,
which hooked into the motivation thing and the confidence thing really nicely because suddenly they're like,
wow, I really made a difference to this person. I'm motivated to do more things and I'm confident I'm doing a good job.
So they were able to pull on a couple of different mechanisms, um, in order to present data that was meaningful and useful to them.
At the end of the day, when it gets into kind of like meaningfulness of data and usefulness,
and a lot of these components co-production of the process and understanding in that collective sense making thing that kind of talked about.
I have a whole other lecture on citizens making I didn't get last week.
I didn't bring it in here, but we can talk about it. Um, is really helpful to overcome things like threat and fear of data and new stuff.
Because you're working together, you're often having to change with a lot of different people.
And if you all understand that differently, it can feel very threatening and very scary.
Same thing with presenting data.
It can feel like it's being punitive if you're not having a conversation about what it means and why it's valuable early on.
To help. Yeah. Yeah.
Really quickly. And thanks. This is really fascinating.
I'm wondering about the organisational preconditions, however. To what extent and to what level of success can you have?
This framework placed atop an organisation that has not yet figured out the organisational preconditions?
Yeah, I mean, it's it's such a wonderful question.
And I think one we just are still grappling with because we don't have enough learning health systems right now to do comparative work.
And the ones who are doing it already have the preconditions in place, for example, valuing data and valuing research.
And, um, having a different view, like having that safety kind of culture and,
um, kind of what Amy Edmondson talks about being like a fearless organisation.
They already have that. Um, I have yet to see an example of an organisation go from being top down punitive into this model yet.
Um, I think she's she's keen to find it. So one of the things she's been doing with some colleagues at McMaster is building a network of organisations
that are either already identified as a learning health system or aspiring to be a legal system,
and she's trying to they're trying to unpack that next piece. But you're right.
Some of these like the context pieces and some of the preconditions, um,
might rest on our organisational cultures that are very hard to move and sometimes very driven by particular types of leaders.
Um, so they're also trying to unpack that piece as well. And it's tricky.
Not mention. There's also political, uh. Um, so very cool work.
I highly encourage you to follow rhythm. Um, another piece of work we did this is older, but this is also this is more of a team level, um, mechanism.
And we actually did a trial of a technology that we had built and co-produced with patients,
their families and primary care teams to do goal oriented care.
So shifting a model of care to asking patients what matters to them,
and then wrapping care plans around them and monitoring them against goals that they cared about.
So that's goals like, I would like to walk my dog every day, not I need a lower HBA one C number.
Um, and so we went through many, many iterations of this.
And at the end of the trial, we had lots of people using it and lots of people not using it,
and the people who really used it who really changed behaviour, both the clinicians and the patients had this very strong alignment of coherence.
So that became a mechanism we care a lot about. And we have been building into kind of trials of our next rounds of technology.
Um, because we often build a technology for like one user group.
But a technology might actually be interactional and asking multiple people.
And when we saw the highest amount of use from both the physicians and in clinical teams and patients like people we called super users,
they were using this thing every day, whether they really needed to or not.
They had this really strong alignment between their sense of coherence, both at the individual and team level.
It made a lot of sense to them.
The tool did, and it made a lot of sense to their relationships together, and that changed their behaviour really meaningfully.
So this we've kind of reimagined normalisation process theory into this framework from a huge narrative analysis of a ton of data.
And to actually get to something that looks pretty simple.
But at the end of the day, that's moved us to think differently about technology, around what's meaningful to people.
And that's a mechanism we kind of chase now, uh, we try to build it into the way we build the innovation.
We build it into the way we do our implementation work as well. Meaningfulness really matters.
Even in technology. They're not inert. They matter. They're things.
They're real social artefacts. People have feelings. Lots of thoughts and feelings about our technology.
Okay. The last one we'll talk about is kind of diffusion and adoption mechanisms.
So this is where things get like really fun and sticky where things are just like dispersing out whether you can measure it or not.
And um, there's so many fun studies of complex adaptive systems theory, um, where you can measure the first couple of stages.
And as it gets further and further into embedding, it gets really, really hard to measure,
um, which is the fun and challenging thing of a complex adaptive systems theory.
But this is getting into bigger stuff like tipping points and betting and emergence social proof.
And so diffusion of innovation is helpful for us here.
The dynamic sustainability framework for us is helpful here. And of course, complex adaptive systems theory that's helpful for us here.
All of them suggest why things go from one place to the other.
So mechanisms here are social contagion, norm cascades, institutionalisation of change that occurs over time.
So this is when our change is getting bigger and bigger. This is actually work from Scarborough.
Um uh, care that says uh, from 2022.
Joe, do you know what this is? I know Harry. Yeah. Oh, yeah. It's a really nice little piece of work.
Yeah. What's a big piece of work? Uh, that they connected micro and macro level phenomenon.
Um, so they were looking at mechanisms that were occurring locally that cascaded into mechanisms that were happening more broadly within the system.
Um, which was super cool. It's a great paper. I highly recommend reading it.
And they found things like learning, adapting,
and institutionalising that occurred in a local space would also cascade into much
bigger change of the social level over time when they were looking at the literature.
Um, and they embed, you know, Rogers diffusion of innovation theory and a couple of other theory which make the
ultimate impact on the structure they build in cognitive and normative other,
uh, theoretical frameworks to, um, to show how those kind of key mechanisms cascade forward into something bigger.
Um, I have another wonderful student, um, Marsha Gupta, who, um, finished a PhD some years ago.
She ended up writing like five papers on her PhD. And here's two of them.
She looked at kind of the emergence of family medicine in India for her work.
So fascinating. Um, I highly recommended your diffusion of innovation theory,
for I was her theory nerd on her thesis because she was mostly supported by other family docs.
Um, and she did some quality. These are her qualitative,
descriptive and survey studies where she looked at emergence of family medicine over a 30 year period and was able to find
this link between the work of innovators and early adopters to do institutionalisation work that allowed family medicine to,
like, take root in the entire country. Um, so it the potential that so she didn't go at it from a mechanistic lens.
But you can kind of apply some of that coherence, um, criteria, um, to understand these as mechanisms,
things like having sufficient training programs that were widely dispersed was really critical,
engaging a multidisciplinary model because of the very different kinds of discipline approach and disciplinary approaches to medicine in India.
Also absolutely important to kind of embed into the norms of the society there.
And then having increasing postgraduate training was a way of institutionalising it into the entire system.
So over this 30 year period,
they went from not knowing what family medicine was to having a burgeoning group of clinicians who were delivering care all over India.
Really nice piece of work that shows this kind of how mechanisms can occur in these much higher order ways.
So these things all matter and they're really cool. But they also shift how we think about our strategy.
So not only understanding why something happened.
Um, I like to talk to our leaders about they, they also tell us maybe which strategies you should target,
because it's going to pull that lever of change for you.
So a lot of leaders kind of intuit the different strategies you might use to activate a mechanism.
But the difference between using theory and not is if you don't, you might be trying eight different things,
and if you do, you might only have to try to in order to actually get something going.
So I have an example from that technology example.
I you the core of two ways we were thinking about this. So with when we were doing this at the primary care level, there's our goal or indicator tool.
A really important enabler for us was getting clinician champions.
So I'm sure you've heard this before. Clinician champions matter a lot.
And at our primary care teams in Ontario, they kind of function somewhat independently, but they're very much a cohesive team.
There's usually like a person not even with a title that's like a leader, but just a person in the team that everyone looks to for guidance.
In some of our family health teams, it was the social worker and some of our family health teams, it was the admin.
Sometimes it was a particular family doctor.
Um, so we were getting them on board to this technology.
They kind of came in and championed it, and then they got the whole unit on board so we could run the trial.
So that was a really important mechanism for us to pull on. So, you know, fast forward five, six years later,
and we're integrating this technology into a care planning tool at a hospital in two
hospitals to support discharging from hospital to home for same kinds of patients.
Very complex. This is work we did with Trillium, and we're doing it on my hospital at Finding Health.
So we've gone through all of this co-production work,
worked very closely with our hospital teams as well as our primary care teams, and we are ready to get this thing going.
So we went to the back to the same strategy. We said, okay, here are our clinician champions.
We identified them on the units across all of these different hospital spaces.
And the champions were great champions and they continue to be great champions.
They were rah rah, rah rah for this thing the whole time. But people really weren't signing up for a trial.
And we were at this for months, and the champions did everything they could to talk to everybody.
They invited us for lunch and learns. They invited my team to be on the units all the time, but nothing was really sticking.
And then kind of one day the hospital decided to do a communication about the digital bridge project, and they have highlighted, um, us.
They highlighted the wonderful students that were kind of all over the place, trying to work hard to recruit.
They talked about its connection to Sinai Health and what was important to Sinai, and advancing compassionate care and connection to the community.
And suddenly 20 people signed up for my. Yes. So that tells us a very different lever of change in primary care teams.
It was about the person that they knew and their connection to that person that they knew, that made that behaviour change happen for others.
For this environment,
it was their connection to their hospital and their sense of being a team player with their hospital that hooked them in to change their behaviour.
So, you know, we didn't know that before, but now we do that.
Maybe before you go out and set up a strategy or an adoption strategy, you ask people like, what's the most meaningful thing to you?
Like, who do you go to for help? What makes you feel like you're connected to this role and to this organisation?
And if you ask those deeper questions about what drives people and what makes them want to do new things, then it changes your strategy.
And we would have saved like eight months if you talk to someone.
If we had just asked the communications team to write a little blurb about us much earlier.
So the question that's just popped in that, I think,
relevant to the whole question of saying when you're doing implementation work, it can be that multiple mechanisms are at play.
So when that's happening, how do you how do you navigate appropriate models or do you incorporate multiple models and frameworks in this case?
In other words, how do you work through the complexity of the real world messy implementation?
Oh, talking about okay, what a wonderful question.
I think I have a slide coming up on this. Um, I do have a slide coming up on this, but, uh, there are people who, um, remember the name.
Anyway, the names on the slide, um, that you can embed lots of different, um, theoretical frameworks.
They do need to be epistemologically aligned. Um, that becomes really important.
Um, whether you're doing research or you're trying to guide mechanistic thinking, because some theories rest on some assumptions,
and theories can rest on conflicting assumptions so important to see that they conceptually map together.
Um, but you can kind of work them together if they're meeting,
if they're not in conflict and they're meeting a gap that each other don't let each other aren't hitting.
So if you want to like, layer cognitive and social theories together and are not really in conflict, that can help.
And you see that happening in research, right. Um you'll see com being linked with for because com is not doing a good job of the environment.
So they at C version get to the environment stuff.
Um so you can the challenge and I don't know that I certainly haven't unpacked it and I haven't seen it unpacked yet is what you prioritise.
Um, so if you have many mechanisms at play, which one you pick first, whether they want,
there's evidence that they actually can chain and cascade and some mechanisms need to happen first and others have happened later.
But it's still kind of a messy literature. Unless if somebody like read something that I haven't read yet and I'm not sure.
Yeah, there is automatically. So we, we tend to go with, um, I like to go with talking to people and asking them like what matters to them the most.
And then I prioritise that, like what they're thinking about in that moment, what the organisation cares about in that moment.
And then I'll try to cascade, um, kind of strategies that linked to those mechanisms first, um,
that can be aware because ultimately we're just trying to get people to do something different.
Um, so you need to hook into the people. I am very possible.
I do encourage going back to assumptions so we're not in conflict.
All right, I'm gonna make you do more work. Okay, so here's a drawing of how you could potentially draw mechanisms.
This is, again, Carroll Lewis's work. They're pretty intense, but if you're a visual person, they can actually be very helpful.
You don't need to know all the components. So you had your, um, your list of thing.
Your thing. The stuff in the middle and your outcome. You can actually logic model these guys out.
You can see about how they're connecting. Um, here are I going to describe all the different.
So you might just want to pick one. So here you might have either you're saying or your strategy whatever you're putting in place.
This is maybe something like a preconditions. So like organisational preconditions we are sort of talking about from Maurice's work.
So what actually has to be why maybe you might just need the funding to do the thing at all.
Um, without it, nothing happens. Anyway.
Uh, then you have your mechanism, but we are just chatting about important things that we need to think about with mechanism.
So there are these moderators and mediators that Carol Lewis and company and other theorists in the space like to talk about.
These are factors that can turn up a mechanism or turn down a mechanism or shut out the mechanism entirely.
So sometimes, for example, something might be very meaningful to a person, but it's not an organisational priority.
So they're not given any time. So that's shut down that mechanism entirely.
Um, or sometimes there's no political will to do the thing.
So shut down entirely. So these, these are things that can come in and just sweep away because they have more power or more, um,
ability to either take away a precondition or kind of make the mechanism mute for that particular moment in time.
Let's do some drawing or. Super fun.
I won't do it. Um. Pick one. Just pick one mechanism.
Chat with your friend again. Those online. Please go ahead and find a piece of paper and you will give it a go.
Try to pick one mechanism. It can be at any of the levels we talked about.
Could be a cognitive one. Could be a team one organisation social system.
Um, the cognitive ones are sometimes a little easier to start with because you can feel them in your body.
So those I don't see. So, um. Don't worry.
You know, you can't put a determinant in there, but I think we want to focus on these areas outcome moderators, mechanisms and either strategy.
So does any want to share anyone want to share with where they got you. There's no right or wrong here.
So I'm going to do a little bit on what they offer us. And then I'm going to go to our wrenches.
Um, so remember so I've already talked about this. Remember this study before because we know this now we can do different strategies.
So I gave an example of that with our technology. But so say we're doing virtual care.
If I know that virtual care hinges on professional identity.
And they told me that they were training to be physicians in a particular way, well, then I need to train physicians in a different way.
I need their identities to be not. I'm not a doctor in a room.
I am a doctor that gives care in multiple modalities.
That's a different way of socialising people, and that's a different kind of strategy that you would have to think about kind of earlier days.
Um, so mechanisms can help us tailor our strategies.
Um, they can also help us understand failure. So this is a fun exercise.
If it was to do in all your humility when something goes wrong in so many of our trials, do not land the way we do,
we will have to do this for my trial of our digital bridge project, because there was a lot of implementation failure.
Um, and this was one for a clinical decision support tool where they did all the right kinds of strategies.
They did their clinical champion, like the example I gave, they did their training.
Um, and yet it failed. And they were able to uncover mechanisms of failure.
And in these cases, it was a lack of confidence in the tool.
So similar to Michelle's example, um, that can lead you down a you thrive or survive, pass fail.
I'll talk about in a second. Um, and competing clinical priorities.
Both of those things got in the way. Um, this is a great paper.
Um, that came out if you're if you're keen on, um, you know, failure from us that that came out just earlier this year.
What you'll see in these mechanisms papers as you get into them.
Um, and I think some of the conversations were coming out like this that mechanisms
can get changed and there can be many of them within a particular intervention.
So you can actually have five, 6 or 7 of these, um, all in one row, which is anyone have kids?
I said 6 or 7. And I can hear my daughter, like all it's her whole life.
Right now she's ten. Um, mechanisms can also cascade.
So we have that. Ah, um, that example before um, this I academic,
you and company did a lovely scoping review of employee driven innovation a little while ago, and they,
um, I was very kindly invited to write a commentary on it, uh, where we, uh, I looked at the literature a little bit and showed how, you know, um,
mechanisms happening within an employee driven innovation space can cascade into the same things that drive large system change,
things like distributed leadership achieving better fit between innovation and local context in alignment,
um, uh, around kind of what is perceived to be meaningful that if you do that is small, that can cascade into bigger and bigger places.
Those are mechanisms that can potentially transfer into other spaces.
This is a mechanism I'm a little bit geeked about. This is Alex Honnold climbing type 101 earlier this year.
Anybody watch that while sweating? I was on I was on my bike trainer at the time which was not a good idea.
My heart rate was too high. And so when I've been working on a little bit and here's a drawing I've done for you.
Sorry. Online. No, I'm off camera. Um, is how threat that exists at different levels can lead to different kinds of responses.
So some people have kind of theorised this,
again from a cognitive psychology space where they've shown that and particularly in accelerated accelerated change environments,
that we can elicit this threat response, which can lead people to survive.
So that's like when you present a new idea in a room and either you or somebody gets like angry or shuts down or looks at their phone or leaves,
that's like your fight, flight or freeze. Um, example of when something new was presented and it feels threatening.
Or you can go into a thrive space where you embrace the fear and climb this crazy building that looks horrible.
Then, um, in implementation science, it's looking like things like resistance or anxiety or loss or perceived risks,
but they're kind of sitting in that space of threat a lot.
And the reason I'm interested in this is it seems to be happening, um, in technology environments that when you present,
especially when somebody has gone through a really bad technology implementation before you present something new and it's a total shutdown,
like an actual threat response to something that they don't want to go through again.
And so it's a mechanism I think it's under theorised. And I'm working on a little bit, uh, around the nature of threat in terms of where it sits,
not just at the individual level but at multiple levels, and that it can look like different things.
It can look like identity, it can look like norms, it could look like survival at an organisational level.
And so if I were drawing a mechanism drawing, I mentioned one I've been working on for months.
This is one of them you can draw. So say someone's going through tool training.
Maybe they're a radiologist. And that is leading to some rule uncertainty, whether they're actual and uncertainty being a precursor to threat.
And then they feel some professional identity threat. And now we're either going to go into survival mode or thrive mode.
So our Meccan our moderators in this example would be their past experience.
How have they seen something like this before and what are they likely to expect?
And then how meaningful this thing is to them in relation to their perceived identity and threat.
And so when I've seen meaningfulness turned up, that's a dial that can go up.
Thrive happens because they're willing to tolerate the risk and the uncertainty, because the outcome of the outcome,
when their past experience suggests this is going to go terribly, they're more they seem to be more likely to happen here.
So this is a lot of what I'm lecturing on in the next couple of months at different places.
And I wanted to share with you guys because it's a learning process, and I'm also keen on what you guys think about that.
Come on, come on. Them. Yes, please. Since.
I like it. Um, I think.
I think we we all can think of an example where somebody has gone into that freeze mode because of a
bad experience with the technology that was introduced that then kind of completely wrecked that job.
And then they were then doing things they didn't like and didn't.
But also the effort you put into the technology and then the whole thing fails.
Um. I'm not sure that meaningfulness is the only thing in that full.
I remember we were doing a study years ago, uh, which was a seven site,
randomised controlled trial of telehealth in heart failure and was going well in six sites and then site seven, which was a plane ride away.
Um, nobody was being recruited. So Joan and I got on a plane and went off to this place where they weren't encouraging anyone.
We had all these kind of unusual things about, like, what? You're recruiting.
And then we picked up the stories, and I think we got the stories in a taxi from someone, you know,
some of those informal things, the story of something that had been introduced four years ago, that was just a disaster.
And then everyone got blamed and punished and and people were still in that freeze mode.
Now the challenge for us was not just to make this meaningful, but also at a technical level,
to reassure them that the the technology that we were introducing was not going to go as badly wrong as the technology that was in Chicago.
And actually, one of the things was that the technology had moved on, but that it was a much, much better technology than the one.
Yeah, that was being introduced. So it wasn't just meaningful.
It was like, is this thing going to work because it didn't work last time?
And value. So. Yeah. Yeah. Yeah. You know.
Whatever. Yeah. That's great, I love that. Um, I have a whole lot of things I like.
Narrative approaches is really important because stories matter.
Um, I love that. I'm going to ask you more about that. Um.
Thank you. I'm collecting stories right now. I'll come back to that.
Um, yes, because that's looking back into past experience and that's changing how I kind of value meaningful,
because there are some challenges for our field that we need to keep going. And I want to elevate these because you're probably going to experience.
And if you're going out and trying to study mechanisms, one is the definitional clarity issue.
Uh, we still are missing it. So if you're going out just just pick one that resonates with the the environment that you're working in.
Um, if you're implementation, hang out with Lewis.
If you're realist, hang out with POS mentally and um, kind of recognise that this is still an evolving space.
Duncan also suggests to us that magazines don't just get turned on and off like layouts, but they're actually probably more like dimmer switches.
That there's actually a continuum of mechanism that can actually exist in small ways and big ways.
And then and the context matters in that in those instances and conditions by and all of it.
It's a dial. Other work I did with Jay, um, some years ago, this is back when we were doing postdoctoral work.
Yeah, I grew up in here. I know he and I chatted, but we were very, very close colleagues.
We work together a long time. We did some work understanding kind of mechanisms or can milestones, as we call them, advancing integrated care.
And because we looked at it over time,
what we are finding was that something that was a mechanism at one point in time actually became a context at another point in time.
So mechanisms actually have like a time limit, or they shift over time in the way they, you know, actually have their action shifts over time.
So you have to consider the time component. Here's the study I was mentioning before about how you could layer on different
kinds of mechanisms that are cognitive and institutional and process oriented.
This is of course in all paper. This is them just looking at, um, uh, spread of an intervention.
They looked at nutrition programs, just seven that that spread.
What you see in the middle is what the tests are important for scale and spread.
And then these are all the mechanisms around those seven things that mattered.
Um, in order to make Scanlon spread happen, you can make it a little deeper.
It's a very cool paper. Very. It's like a lot.
But it does tell you kind of what how these things relate to each other and how they kind of come to some of those bigger order.
Often our guidance sits at this really high abstract level, and you don't know why it matters.
This is a nice paper that tells you kind of why it matters. And they use multiple theoretical lenses to help us unpack that.
Um, a last piece is that there are often invisible and consequently very hard to measure.
Um, this is a nice paper by Hull and Company.
Um, where they were looking at mental models and implementation science.
Mental models are very hard to uncover. We did some shared mental models work, and it's a lot to uncover in in this very deep enough idealogical.
Um, but they were finding that it's really critical and implementation science.
So we need to start thinking differently about how we unpack mental models.
Well. So to summarise, cosmic uh, determinism does help describe terrain.
Our strategies help describe action, but our mechanisms explain movement.
Like my kids running around the beach in Belgium just as we connect. The three things you can think about going forward are.
Implementation science might be at an inflection point, or we need to pay more attention to this.
Mechanisms inform strategy. Design can be essential for system level reform.
And finally, studying mechanisms requires theoretical pluralism and possibly methodological innovation.
Reminder. Thank you.