 So today's webinar environmental influences on mental health path analysis for contextual implementation, which is presented by Dr. Daniel Dan, who is an urban planner with expertise in community engaged research and place based interventions for health equity, among less advantaged older adults in community housing. He completed his PhD at the National University of Singapore on neighborhood atmospheres and affordable housing and their impact on well being. SFU gerontology, Dr. again, further validated and extended his transdisciplinary neighborhood health framework to include cognitive health and examine CLSA data using contemporary effective approaches and sex and gender based analysis. He is a CI HR fellow and research and knowledge translation in urban housing health and winner of the 2021 CI HR I fellowship prize of excellence and research on a drink. So with no further ado, I'll let Dr. gam get going. Hello everyone. Thank you for having me here today. Let me just share screen. Alright. Okay. I will just pick up on a few things in this title. First implementation and mental health here we're referring to both depressed symptoms, life satisfaction, and also things like combination cognitive function. And the other particular thing in my title, which is really, really long, which is given by CI HR is that you see a bit on knowledge translation and urban housing. So putting all this few things together, it's really quite a big, heavy thing. And this is what I will be attempting to do today. In addition to my role at Simon Fraser University, I'm also founder and community planner at healthy aging in place and nonprofit. At the end of this revenue, you will be able to identify individual and environmental levers for mental health promotion. And name suggests ways to overcome potential confounders of interventions in community settings and explain the usefulness of path analysis. For example, to identify relevant intermediate targets or to systematically develop interventions for translation across context. Okay. I'm often asked, what is community planning? And I find it best to explain it this way. It is primarily a type of social planning, but it extends it into the field that perhaps people in the room are a little bit more familiar with or not so. So social planning is a discipline in urban planning that focuses not so much on the built physical environment, but the social environment, which is also in a sense built because we construct this social environment that we are we're in, and the kind of systems and the kind of services and provisions that are available. So social planning focuses on the non physical built environment. You can think of it that way. You can just call it social environment. But, but we know that that's just one approach to things and then there are many other approaches that are relevant in the community. For example, a lot of prevention or health promotion happen in community settings. And one of the most cutting edge areas called implementation science. It has been around for a long time but recently has received quite a bit of attention because of the promise that it can translate research impact directly into our communities. Because we don't want to do research for knowledge sake alone, but we want this knowledge to be mobilizable and mobilized into the community. So implementation science is the study of how programs translate across context. What caused it to fail? What caused it to succeed? And the typical approach now to implementation science is you think of a program, you try it in the community, and then you get all kinds of feedback on whether or not it works or doesn't. From barriers to quantitative outcomes on health. And then after you write that report, you think, okay, now that I'm going to implement it in a different community, how might I do differently? If you have a community psychologist in your room and your intervention is quite psychosocial, psychological or social, then they will say something different. They would say, hey, actually the communities are not all the same. The order in which you test out your interventions matter. And if you test out a neighborhood of this characteristics and then later test it out in a different neighborhood of another characteristics, you might want to adjust some things. So that is what I call community planning. I'm extending social planning into these two overlapping and extending fields. So that's a lot for now and it might be a little bit difficult to make sense of what I'm going to present next. And so for that reason, I'd like to invite you to take maybe half a minute to choose one of the following three things on the side. Imagine in this presentation as you are listening, a policymaker working at a large provincial level deciding how to allocate budgets of money to different places or programs. That's one. Number two, imagine you're a director of seniors program at a community organization serving one particular neighborhood. And you get to decide how much resources to allocate to different activities or how you want to run your activities or how you want to recruit and identify specialty data or structure programs to keep your clients, the older adults happy. Or three, maybe you are not an older adult yourself. So you're not above 65 for the purposes of definition if you'd like to define it that way. And then maybe if you're not there, then maybe you can choose number three. Imagine you're an older adult with different kind of experiences, some healthier, some not so and different kinds of constraints. Maybe you are retired, maybe you are not and maybe you have lots of wealth or not so much. So how would that change how you receive some of this information and the kind of questions that you would have. So yeah, this is my way of keeping this webinar interactive and I hope to have that kind of questions from you to see where we go together. So I hope you have chosen one of the three policymaker, director and older adults. And then now imagine this just to help you get into that scenario. Imagine this is the map that you can choose from and on the maps you see different colors representing different degrees of social economic deprivation and different kinds of well-being. And this is a partial map of British Columbia around mainly Metro Vancouver actually. And you reside or you are charged, you are taking charge of a portion of this map or you reside in one of these neighborhoods or forward south Asian area. It's a postcode prefix, the first three digits of our postcode. So one of, you are one of the three roles in one of this area or a region. All right, now we're ready to begin. So what we do in this webinar will be there will be three parts. First, I will highlight the needs for intervention among older adults age 70 and over with this study that is published on the pressure symptoms with my colleague John Bass. Dr. John Bass is an excellent bio statistician, so humble and so excellent to work with. I've learned a lot from him. And the two other studies that I'll be presenting are path analysis. And this is the first one is on the individual level and the second one is on the neighborhood level. And it will become a bit clearer as we go along and then we will have a discussion at the end of this webinar. Okay, I hope you're ready to begin. The first study is age and sex trends and depressive symptoms among middle and older adulthood. Okay, so we are trying to find out what are the trends in depressive symptoms and depressive symptoms are basically how often you feel sad, how often you feel like you can't get out of bed, how often you feel like it takes a lot of effort to do something. So those are examples of depressive symptoms. And here we're using cross-sessional baseline data from CLSA and two other national or regional longitudinal studies. And that is the health and retirement study in the United States and share, sorry, health and retirement studies in the United States and share in Europe. Okay, and our union of analysis here is individual, nothing extraordinary here. Typically, most of our studies have individual as a union of analysis. And here we're looking at middle age and above. So according to CLSA, so this is a very descriptive study. According to CLSA, you will see that there is a curve in depressive symptoms. So throughout your life depressive symptoms will decrease. This is not really a trajectory, but this is a trend based on each age. At each age, yeah, sorry, that's hard to say. So at 50 years old, what is the typical depressive symptoms? At 60, what is the typical at 90, what is the typical so on and so forth? That's what we did. And we modeled this out into this, where we visualize it into this curve, of course, with some appropriate adjustments. And what we see here is that it all throughout our lives, it goes down. Depressive symptoms decreases until a certain point where it starts increasing again. So depressive symptoms have a U-shaped curve in mid and late life. And that is something that has been shown in various studies, but it has never been studied to this detail. And we found out here that it increases around age, I would say 75 year. And that is the Canadian cohort. And there is a difference between male and female in the Canadian cohort in the American cohort, which has a different way of computing depressive symptoms. Here we use 10 items, here they use eight items, which is kind of like six minus two. And so the numbers here, the absolute numbers here are different. So don't have to read too much into it. It doesn't mean that Canadians are generally more depressed. It's just the way the skills are. And here you see also a slight upward adjustment around age 75. So it seems that 75 is the number in North America where older adults start to experience increase depressive symptoms. Now, if you're one of the three roles, your question might be why? And if you're a policy maker, you probably want to know what you can do to make it better. If you're an older adult, maybe you want to ask other older adults is that of your friends, whether or not they do experience such things as you mean you don't, or if you are older adult and you're experiencing increasing depressive symptoms, then well, you know you're not alone. Okay, in shared, the turning point is slightly different. I would say it's around 60, maybe 65, not very sure here. And again, we see a difference between male and female and then towards the end of life or towards the late old age. Sorry, I shouldn't say that. Towards much later life, it converges. So this has some implications, especially if you're a feminist and more of us should be. And yeah, one way to think about it might be there might really be no difference. It might just be a measurement artifact. It means maybe women are more truthful and then more willing to share the depressive symptoms when they are reporting in a survey, or it just might reflect the truth. I say it could be either of both and it's probably a combination of both. It might reflect the truth, the reality out there that maybe females experience more depressive symptoms. So it's a combination, it's likely a combination of the two. And if it's a letter, it is more so the letter, it is indeed a cause of concern. So we should, if you're a policymaker or if you're a director of seniors program, you might want to find out ways to include females, right? Then again, somebody might say it's a survivor bias. Maybe males live shorter lifespan in general, by a few years. So maybe that's one of those. We'll stop here. We can have more discussions about that later. So this is the background of the state we're in, or the kind of environment that we're working with. And so if we are concerned, we're gentlologists, we're concerned with the well-being of older adults, we would be especially drawn to what's happening here. Okay, that's the background, descriptive study. Nonetheless, very important. And in this second study, in some sense also descriptive, but a little bit more complex in the analysis, in the sense of we're trying to find out what is the mediation effect. And I do this through what we do this through what we call path analysis. And I want to acknowledge who I have called us at the end of this presentation. So environmental influences of life satisfaction and depressive symptoms. So we add life satisfaction here. Among older adults here, we're looking at 65 and above only with multi-morbidity. And I'm particularly interested with loneliness as a potential important mediator. And then you'll see that the mediation analysis we do here is a little bit more complex than the normal ones. We have four levels instead of three. And here we're going to use longitudinal baseline and follow-up. One data, again, unit analysis individuals and ages 65 and above with multiple chronic illnesses. And this one, for example, there are total lists of 27 that can be derived from CLSA data. This can range from diabetes to high blood pressure to cancer and things like that. Yeah, many, many types of chronic illnesses from more severe to more common. Okay, so I'm going to present my results in two path diagram. And this is the first one on life satisfaction. So here my outcome is put on the right and then my predictors are put on the left. My mediators are put in the middle. And so my theorized causal direction is from left to right. So here we have housing and neighborhood factors that intermediate variables that I'm interested in, and then psychological resilience on the, on all the outcomes on the right. So the outcome and intermediate variables, which is this three columns, control for each sex, education and baseline, if they're significant at zero order. That means if they, if we do a simple regression, univariate regression and then they are correlated, then we will control for the relevant variables. We cannot then we don't do so to avoid introducing extra unnecessary complications to the, to the model. So, and so you'll see here that my control variables are not shown in this path diagram. That's just for simplicity, because our interest is in how these variables are on this diagram relate to one another. As you can see, there are many arrows and there are many numbers. And these are better coefficients. And this is the P value. I should have included here the range of the coefficients, but I didn't do so for this study. Maybe, maybe just a bit. It's good. So there are a little bit too many numbers here. There would have been three times as many numbers otherwise. So anyway, so we have a very clear and this is published. And I will show you the reference at the end. We have a very clear idea of what we want to test and why we want to test. And we found from different articles that study mediation or correlation studies that these things are related. And then these things are related and then these things are related or these things are related and then they are mediated by this. And these things are related and they are mediated by loneliness or these things are related and they're mediated by all this thing. So we find different types of studies. these, and then we try to synthesize them and create an overall hypothesis or overall picture of what might be happening. Based on that picture, we put things in, put those variables into this columns in this particular order, and then we allow, we run path analysis, which is a type of structure equation modeling, if that's familiar to you, if not, you can just think of it as separate, and we allow all the variables to relate to all the variables to their right. So housing quality can go directly to life satisfaction as you've seen here, and it can go directly to social support, which is shown here in this diagram, and it can go directly to walking, which is not shown here in this diagram. That means it is not significant. And so the way to read this diagram is to note where the lines are missing. So that's one way to read the diagram. So for sure, so for one as I pointed out, housing quality among older adults with multimodability with multiple chronic illnesses is not correlated with walking. So if they if they live in a better house, it's not more likely or less likely that they will go walking around their daily environment. But if their neighborhood is cohesive, there is a strong chance that they will experience a lot more social support. So I'm just going with the biggest number here, social support, and then they will experience less loneliness. And as a result, they will have more life satisfaction. Sorry, the number here is three first. Okay, so this is an important path or pathway. Neighborhood cohesion increases social support, decreases loneliness, and increases life satisfaction. You see that two of these three intermediate variables have direct impact, direct relationship with our secondary intermediate variable. And that tells me and these numbers are quite big. And it tells me, especially after we do the effect computation or calculation, we found that loneliness is indeed quite an important variable. So the second model here, we're looking at depressive symptoms. And what we see here that we don't see in the previous diagram is that there is no there is in the previous diagram, there isn't a direct link from neighborhood cohesion to life satisfaction. It doesn't then mean that neighborhood cohesion is not important, but then rather it means that our intermediate variables explain the effect of neighborhood cohesion on life satisfaction very well. So if you want, so this intermediate variables explain 100% of the effect of neighborhood cohesion on life satisfaction. Here, the effects, the amount of effects that explain through this intermediate variables as opposed to and also walking, as opposed to this direct variable is about 50%. So a lot of it still goes through this middle and everything else is indirect through this for intermediate variables. So again, as you can see, neighborhood cohesion seems to be the more important exposure variable and more neighborhood cohesion increases more social support. So they receive more social support as a result and this is measured with the MOS social support scale. And and then that leads to decreased loneliness. This is measured with the UCLA tree items skill. And then that leads to decreased depressive symptoms. Sorry, again, this this particular number should be, oh, sorry. Sorry. Yeah, yeah. This number is correct. The sign of this number is correct. It's just that I would explain that it decreases loneliness and decreases depressive symptoms. The sign here refers to the relationship between loneliness and depressive symptoms. If you have more loneliness, you have more depressive symptoms, which is as expected. So this, again, is an important pathway. So we have a good refined model fit in this case, model chi-square p-value was missing and we have good model fit in this case, which is a way of assessing the rigor of puff analysis because if you run almost, okay, not almost anymore, if you run many models, they will converge and you will have a path diagram to draw. Some of them will have poor indexes, goodness of fit or G.O.F. indexes, and that means that the model is not reliable and it shouldn't. For that reasons, it should not be taken to, it should not be something that you want to rely upon when you're making decisions. Okay, so that is the second study and you see a variety of variables in there and you can imagine how that actually works, right, especially if you're an older adult or if you're a very engaged director of seniors program, you have worked with people in your community and they are degrading to a certain kind of activities where they can make friends, where they can talk about deeper issues, assuming the kind of activities are conducive for them and you sort of realize that, yeah, maybe that's true, loneliness, the psychosocial, social support and social participation, these are the things that really matter to older adults, especially when they are more frail, for example. So now that we ask, okay, if I am going to, if I'm an art researcher, if I'm going to introduce some programs in the community and I want this program to be effective and to be tailored to different communities as some communities such as psychologists would suggest, what then do I do? Is there more data that can help me? And here I say that popular analysis is very relevant because if then I am designing an activity, I can know that my activity should be targeting loneliness, should be targeting social support, of course that doesn't mean that walking is not important, it might be especially important for other populations, maybe who are not so frail and are in their middle ages. But then if my intention is to focus on older adults, then I would try to design activities that target this intermediate and exposure variables, maybe horticulture, for example, that is nature immersion and gardening therapy, horticulture therapy has been shown to improve social connections. So maybe I will do that and then when I'm evaluating my program, I will be evaluating them based on whether or not loneliness has improved. And if I am going to bring this horticulture program into a neighborhood where I know the neighborhood relationships to be more fraught, maybe because they experience a lot of fights, because YouTube from my experience of working in different communities, I know that some neighborhoods find a little bit more or some of them they just don't talk to one another because they don't need to, then maybe my strategy would need to be different, maybe I need to empathize with that befriending piece, to create more opportunities for them to get to know one another maybe before the actual gardening activity, maybe everybody sit around and share a little bit for 5-10 minutes about their week, so maybe their highs and lows or something they cook recently, or something they're interested to plan and why or some interesting memories. So they create more room for that kind of social support kind of friendship that will allow social support. So that's one way we can use path analysis to design our programs. I can talk more about this to Athean. So in this third study, my question is who needs their neighbors? So having worked in or having worked or engaged with different neighborhoods, I have come to realize that different neighborhoods really do have different relationships with their neighbors. And one of my hypothesis is that people with different social economic statuses or neighborhood with different social economic statuses tend to require their neighbors more or less. So there is a difference and that's my hypothesis. You can guess for yourself which way I think is different, who needs their neighbors more? Is it the region or the poorer neighborhoods? And we'll find out in the next few slides. So here I'm trying to explore neighborhood disparities in cognitive function and that is inspired by one of the study that I'm going to show in the next slide not by me. And here we're using baseline data only just for simplicity because that it's still quite exploratory. We will be writing up a long video and cross-sectional analysis in the process. And here our unit of analysis is neighborhood. So from a community psychology point of view or from a planning point of view usually planners work with neighborhoods. They don't immediately think of like I'm trying to benefit individual or the adults or individual residents or people in this area. They're thinking how can I benefit this neighborhood so their unit of analysis is typically a neighborhood. And community psychologists will study the characteristics of communities, groups of people at one time and not so much. So for example they might study things like efficacy. It's not collective efficacy, not self-efficacy, not whether or not I believe I can do something. But collective efficacy in the sense whether or not a group of people can advocate for their own needs. For example maybe there maybe the municipalities somehow forgot about them and that didn't likely won't happen in most places but didn't pick up their garbage for like a whole month. And so collective efficacy would mean whether or not this group of neighbors can get together and write a letter or something to make sure that their telephone calls don't go unanswered. And so that is collective efficacy and so their ability to organize themselves. So those are characteristics of a group of people. You won't use collective efficacy to describe an individual. Of course you can ask the individual what they think of the collective efficacy of the group they are part of but it is not a characteristic of the individual. It is a perception of the individual and it is a characteristic of the group. Yeah, so depending on how we measure it it can get closer to what we are trying to get at. I'm just trying to explain that these variables there are different variables that apply to different units of analysis. So cohesion is a variable that describes the neighborhood not the individual. Well it can also describe the individual that this individual is more a team player as a result maybe the neighborhood can be more cohesive. And there is a little bit of overlap there but when we talk about collective efficacy maybe there is some relationship with self-efficacy but typically the relationship is not strong. So different variables apply to different unit of analysis. So here we're looking at ages 45 and above and so I said this is inspired by a study and this was the study that I came across in the Alzheimer's Association International Conference in last year and Dr. Amy Kine, medical doctor and also a PhD holder shared with us that they they they used the brain imaging data of 950 of the adults in in America and so upon that they they agreed to donate their brains for various purposes and then they found that that based on this organ donors that when they study its relationship with the area deprivation index of their neighborhood it is linked the hippocampal the brain volume hippocampal volume is linked to neighborhood disadvantage. So those people who live in more disadvantaged neighborhoods have smaller brain volume. Wow how did that happen right? How did being somewhere change the size of my brain? Of course one way to one way might be that they have lived there all along and we we know that early childhood factors are probably a very big influencer of cognitive health and here it's a study on whether or not the different kind of risk factors of dementia and in early life there is education less education will attribute it can be attributed to for 7% of the dementia risk and it is likely more and then in mid-life we talk about things like hearing loss don't worry drinking alcohol and in moderate amount is good so yeah don't yeah but but too much consumption is bad don't worry about that. Obesity is a risk factor and then in late life a lot of the things that we've been talking about depression loneliness social isolation physical inactivity these are contributors of up to like 8 to 10 percent of dementia risk and this 40 percent of dementia risks are preventable and modifiable and so if you put on a policymaker hat earlier in this webinar you will want now to know how you can reduce this because dementia is going to be is dementia is going to be challenging for many people and you would want to be able to prevent it because now this study according to Lewinston says you can prevent at least 40 percent of the dementia risk I mean that sounds like almost half or one-third is preventable so yeah that that so your question will be how and and this is this is my answer okay here goes so how and where might be your question which neighborhoods should I focus on and so now I'll start I will not answer the question which neighborhood first but now I'll start with an overall different neighborhoods have different characteristics as I mentioned some neighborhoods are more cohesive some neighborhoods are more green they are more greenery and here we use the NDVI that is a satellite imaging of greenery at growing season I mean use a mean so that their CRSA data can be linked to canoe data that is the Canadian urban environment data for those who don't know and here I use aggregated the the the data from the tracking cohort with four or more respondents in an FSA FSA is the forward rotation area the postcode prefix and I use this to operationalize neighborhood of course it's it's not a perfect optimization but for our purposes I guess this will do and this is what this is the most granular we have and and so greenness is is the measure of greenery on based on satellite imaging and cohesion is something they answered in I think was eight items and and it's things like whether or not you think your neighbors will help you in case of an emergency and whether or not people are friendly in your neighborhood or in there are also some other measures on the physical manifestation of of this cohesion like cohesion sorry okay and here I use three measures of cognitive function and so we must remember that these are all aggregated data that we are measuring we are studying neighborhoods not individuals and so so I adjust for age sex education and language language because semantic fluency particular is is affected by whether or not you use you use English or French to answer that question so that is one minute name as many many animals as you can do they recall as I named you 15 items and a while later after some exercises I asked you to recall for me that 15 items and how many you can recall executive functioning or the mental alternation test here is I asked you to say one a two b three c some of you might have done this before so on and so forth and see how many you can say of course the maximum range here is 26 there's no maximum here is 15 and there is no maximum here and so and after I average them so that I can get a number for each of the 1500 neighborhoods and then they are controlled for average that material and social deprivation scores that is again the link canoe data Canadian urban environment so the canoe data has two measures of neighborhood sES and actually in fact their neighborhood is more fine greener forward sufficient area and they have two parts to it one is material and one is social material is the income level and things like that and social is for example education level or access to cultural activities I suspect that greenery is actually computed as part of like the MADs in the material component that would have to read the canoe documentation for more information to confirm that and so here we see that semantic fluency which is one of the cognitive one of the main cognitive measures that has to do with verbalizing or speaking being in the middle and we see here that greenness and collision both have a certain so if you live in a neighborhood that's more green is it's more likely that you are verbally more fluent and that's and that will that increases your ability to recall better after a few minutes the number of items that was shared with you and then and also to do this mental alternation test so somehow the brain the ability to exercise maybe the that this our speaking or our tongues is is somehow connected biologically or neurobiologically to this or cognitively to all this maybe to practice to all these other cognitive measures and we have a pretty good more of it again in this case so this is the general across all neighborhoods this is what we found and you can see here that semantic fluency completely explain the effects of greenness and cohesion there is no other things no other lines between them there's no lines from greenness to other cognitive outcomes and there is no lines from the cohesion to the cognitive outcomes as well okay overall all across canada 1150 neighborhoods okay now what if we split them according to ritual and poorer neighborhoods let's start with material deprivation so if you are in a neighborhood below 46 percentile in terms of social economic status this is how the picture changes you see now that there is a line from cohesion to executive function and before that the total effects of cohesion on cognitive outcomes is 0.02 i calculate this times the value of this plus this and i get 0.02 and in this case in this poorer neighborhoods the number is 1.11 that's a lot higher that's five times the number that we've seen before and then that's probably because there is a direct effect of cohesion on executive function and then this is in the richer neighborhoods there was a poor neighborhoods and so in the richer neighborhoods you see that oh the amount of greenery actually doesn't influence anything anymore that tells us that people who are living in richer neighborhoods do not rely on their neighborhood so who needs their neighbor according to this analysis so far poorer neighborhoods people living in poorer neighborhoods actually need their neighbor they go to the park they have they if they experience a cohesive neighborhood then by socializing by speaking they probably improve their cognitive function or they maintain their cognitive function so that is one way to interpret the data so far and again you see in this case the amount here it's much lower than the number that we've seen just now okay so that was one way to divide the neighborhoods but what if we divide them according to social social aspects of social deprivation social aspects of the socioeconomic status and in this case with the poorer or the less advantage or more deprived neighborhoods again we see a larger number and also the effects direct effects of cohesion on executive function and yeah you can see the numbers here are relatively large and then here the the numbers are gone in in bad wealth to do neighborhoods where people are educated they don't need their neighbors they neighborhood cohesion has no impact whatsoever on them maybe they live in a more cosmopolitan place where you don't say hi to everyone you meet in the city center or that's just not the network they will rely on but then greenery in this case improve their mental or rather the verbal fluency and also their cognitive outcomes so who needs their neighbors again in this study it tells us that people living in more deprived neighborhoods need their neighbors so if you have a policymaker now you got your answer you will be doing implementation in poorer or more deprived neighborhoods but then more deprived neighborhoods they're not all the same some have better cohesion some don't and if you have better cohesion half your better half your battle is won if there is no good cohesion in their neighborhood then yes you would have quite a bit of challenge to address different symptoms and or other cognitive outcomes and so then you need to tailor your intervention for that purpose so next time when you go into a neighborhood and you want to introduce an intervention you should know what kind of cohesion variables they have and the good news is clsa has this data yeah all right so the the image the map that you saw at the start of this presentation was actually generated from clsa data by actually i think i might have forgotten to cite this particular article but you can reach out to me and i will be happy to to share it with you it's it's by my my boss or my ex-boss and professor andruister who is the lead of the clsa study and so some implications from the study so far cycle social interventions will do well to target loneliness individual level so that's one individual lever and cohesion is the environmental lever it's especially important in neighborhoods with greater social economic deprivation path analysis is useful to identify intermediate targets and to systematically translate strategies across context so we don't go into a neighborhood without knowing what it's like but we already know and we tailor the thing to go into the neighborhood so that other outcomes that we don't measure can come up for example in complex neighborhoods community engaged interventions will be required because not everything is measured in clsa for example this is something that i did and another study and we we dive into 26 qualitative articles and we a true thematic analysis we found that there's this thing called at-homeness that's important for community dwelling individuals and this is broken down into ontological safety whether or not they feel safe and secure in themselves and the related to their their the possessions they have um and and and and how they relate with the things around them the sense of social social citizenship and also they are their psychological well-being so that's another way to say longing um and um and this is this on the whole can be how comfortable they are in um in themselves and their environment so this is available called at-homeness that has this kind of explanations or characteristics but and it has been shown to be important a mid-connective decline which affects many other doubts but then we don't have a measure of at-homeness in clsa here i try to use a proxy of at-homeness in clsa and and and that i use a combination of live satisfaction housing satisfaction and neighborhood satisfaction and when we do find that it's important so this is an example of something that is not measured that would be important and so how you will come to this place of realizing maybe there is something that's important that is not measured is um true community engaged interventions that means you do you don't just go in and think that clsa has told you everything you need to know but rather you you go in asking the older adults the senior program directors or what are your experiences um what what what should we do is this intervention even the right one are the questions even the right ones um and and so then um different kinds of variables really much i should stop now um to conclude as each i'll just leave this here thank you great thank you so much i think uh you uh managed to keep everyone interested and jammed a lot into that presentation which was fascinating i myself from a knowledge translation perspective it's nice to have that uh type of webinar so congrats on all that work um we are turning it over to questions now um if anybody has any questions just a reminder you can enter it into the q and a box uh in the bottom i believe we have one question right now actually it's more of a comment um is and that is whether you have any specific recommendations that have come out of the study you talked about implications but any tangible recommendations for sure um but but to answer that i'm surely might have um a question a poll that would help us answer that question better um yeah so that's a quick poll here um are you able to sorry um sorry um maybe maybe we will skip this poll for now um surely i was more thinking about um the um maybe we can hold this poll for now but um i was more thinking about the question um did you take on the role of uh maybe joanne you can clarify for us are you do you take on the role of a policy maker and other doubt or a senior program director so i can answer your question accordingly or is that a general question so the the participants can't actually i'm correct me if i'm wrong participants are automatically muted so i don't know that joanne will be able to answer that unless she puts it in the chat so i'm maybe just answer it based on it being a general question okay then i will tell you that there are many um answer to this question and i might take up the remaining five minutes or so and so this is the recommendation um okay assuming you are i know that out no let's start with the policy maker assuming you're a policy maker you will want to focus um so if you're carrying out um interventions or you're trying to support uh researchers to carry out interventions you will want to focus on more deprived neighborhoods and you will want to focus on whether or not you want to know whether or not the neighborhood what kind of um amenities or resources are available in the neighborhood whether or not there are greenery where all the doubts can gather and go grow to support one another over time and whether or not that neighborhood is cohesive if the neighborhood is not cohesive then you will need to start to search for um relevant ways to um increase cohesion while you do whatever psychosocial interventions that you are bringing in because especially psychosocial interventions if it's something else then maybe it's it's it's it's not so crucial um and that's because we know that social support cohesion will um radically author the kind of um um the mental health outcomes that you will collect back from your interventions so um so that is policy maker if you're a director of a seniors program you will want to start to see what kind of um activities um are you introducing that have you introduced that are relevant that have um impact on the psychosocial outcomes um if if um your attend your your participants or your attendees or your clients don't feel like their loneliness decreased over time or there is no conceivable way to imagine how that might reduce their loneliness then maybe it's time to look for relevant activities or programs to introduce for for those professors um example might be um there is this uh program called finding meaning at medicine and some people have translated into different um context finding meaning in um house being being a being a homemaker for example or finding meaning in aging and so maybe you can get around get a group of um older adults and have a topic each week and then they will talk around how they um maybe maybe the topic might be appreciation or loss of which many older adults experience um then they can share stories or reflections from um their experiences and then that in that way you I I imagine you get into a deeper um friendship much quicker which is something that in in my experience of community engaged research older adults are interested in um at least here in bc um and then if you're an older adult um oh yes cafe coffee um sorry I'm just seeing this yes cafe coffee shop definitely yes yes that's totally true yes I love that let that comment join um and um if you're an older adult um and maybe you are not engaged or maybe you are very engaged what you can do is you can start thinking about the the the street maybe just focus on the street segment near you uh which which house you know has or which unit you know has um older adult and have you seen them do you know them to do do you know their names do you think that um other people know their names if other people know their names maybe it's fine you don't have to be friend everybody it's just not exactly possible for everyone to be a social butterfly also um sometimes depth is is is difficult with with um with just acquaintanceship but but if you realize that there's somebody who's probably isolated who um maybe no one's there to to ask on them maybe maybe what you can do as an individual of the adult is to engage them um and and maybe not on their door and say hey um yeah just just saying hi I can't know you um so that's one that we see a lot of that happening during covid in the UK people talk about um this app called next door um and also a rise of a third way of um policy delivery and then if you're a social planner um you'll be very excited to share that that there are other solutions out there then just relying on researchers or people in the authority so you are as you as an older adult you can play a role but of course if you're experiencing a lot of loneliness please do reach out in BC there is this number called 211 that you can call um um and and look out for community organizations around you I mean we uh we we have such busy lives so maybe we work or we take care of our children all through our lives and and or and or maybe we just i'm generally um stuck at home in the last two years and we're not aware of the types of activities and programs that are available in your neighborhood and what you can do um is them to do a search of community centers or neighborhood houses and and typically they will have some at least some programs that are free and and there might be a book club and there might be a knitting club or a cooking club or just a walking nature so that's that's something that would be helpful great um well I think that was a that was a very comprehensive response about the recommendations and I think you covered all the main group so that's awesome um it's uh time is up so I think we'll move to ending the the webinar um so thank you again for taking the time to take part in the webinar series uh just a reminder the next deadline for data access applications is June 15th of 2022 and you can visit the CLSA website under data access to review the data that's available as well as any additional details about the application process also if you can please um complete your anonymous survey upon exiting that again will help us plan future sessions um for our upcoming CLSA webinar I believe uh Shirley's going to put that up um registrations and details will be posted on our website in the coming weeks um the date will be uh May 12th and uh and it will be by Dr um Melanie Leviss and uh yeah so we look forward to seeing you then and finally uh remember the CLSA promotes this webinar series using the hashtag CLSA webinar and we invite you to follow us on twitter at at CLSA underscore ELCD and I hope everybody enjoys the rest of their day