Ketan Shankardass

Objectives: This study tests whether and how the Canadian public health workforce differed from the Canadian general population in their political attitudes, worldviews and policy preferences related to the COVID-19 pandemic. Methods: Nearly identical surveys were fielded to the Canadian public health workforce in 2021 through leading public health associations and to a census-balanced sample of the general Canadian population in February and March 2021. through a consumer quota sample for a survey of the general population. Results: The Canadian public health workforce demonstrates systematically more left-wing pro-egalitarian, anti-hierarchical, anti-individualist worldviews. Those in health promotion positions are slightly more to the left than those in non-health promotion positions. However, the public health sample did not uniformly favour stricter COVID-19 containment and prevention policies than the general population. When modelling COVID-19 prevention policies as a function of cultural worldviews and ideology, it appeared that these had a greater effect on policy preferences in the general population than in the public health sample. Conclusion: The public health workforce is more left-wing than the general population and professionals in health promotion positions are more left-wing than others in public health. But the public health workforce did not uniformly prefer stricter COVID-19 prevention policies. Cultural worldviews and ideology were stronger predictors of policy preferences for the general population than for the public health sample respondents.

Setting: Setting. Health system leaders in Canada recognise that quality improvement alone cannot address health inequities. Intersectoral action, which involves coordination and collaboration across public, private, and third-sector organisations, can improve the distribution of social determinants of health (SDOH) and thereby, health equity. While Health in All Policies (HiAP) promotes this approach, critiques and empirical data highlight implementation gaps over whether health equity is actually being improved. The potential for HiAP initiatives to reduce health inequities can be strengthened by paying greater attention to how these interventions are designed, implemented, and evaluated. Intervention: We developed and pilot tested a self-study tool that helps organisations learn and reflect on how health equity can be targeted in intersectoral initiatives, including HiAP. This is not the only tool that can be used to consider ways to integrate health equity into intersectoral action; however, it is the first one designed for HiAP initiatives specifically. Outcomes: The self-study tool asks the user to reflect on a series of health equity concepts to raise awareness about opportunities to better integrate health equity into the design, implementation, and evaluation of intersectoral initiatives. Implications: The survey and appendix can fill in the gaps of other tools meant to support intersectoral action for health by focusing on ways to strengthen the health equity potential of initiatives. Users can apply the tool prospectively and retrospectively to explicitly target specific criteria to improve how their interventions focus on and potentially address health equity.

Background: Contextual variables that capture the characteristics of delimited geographic or jurisdictional areas are vital for health and social research. However, obtaining data sets with contextual-level data can be challenging in the absence of monitoring systems or public census data. Objective: We describe and implement an 8-step method that combines web scraping, text mining, and spatial overlay analysis (WeTMS) to transform extensive text data from government websites into analyzable data sets containing contextual data for jurisdictional areas. Methods: This tutorial describes the method and provides resources for its application by health and social researchers. We used this method to create data sets of health assets aimed at enhancing older adults’ social connections (eg, activities and resources such as walking groups and senior clubs) across the 374 health jurisdictions in Catalonia from 2015 to 2022. These assets are registered on a web-based government platform by local stakeholders from various health and nonhealth organizations as part of a national public health program. Steps 1 to 3 involved defining the variables of interest, identifying data sources, and using Python to extract information from 50,000 websites linked to the platform. Steps 4 to 6 comprised preprocessing the scraped text, defining new variables to classify health assets based on social connection constructs, analyzing word frequencies in titles and descriptions of the assets, creating topic-specific dictionaries, implementing a rule-based classifier in R, and verifying the results. Steps 7 and 8 integrate the spatial overlay analysis to determine the geographic location of each asset. We conducted a descriptive analysis of the data sets to report the characteristics of the assets identified and the patterns of asset registrations across areas. Results: We identified and extracted data from 17,305 websites describing health assets. The titles and descriptions of the activities and resources contained 12,560 and 7301 unique words, respectively. After applying our classifier and spatial analysis algorithm, we generated 2 data sets containing 9546 health assets (5022 activities and 4524 resources) with the potential to enhance social connections among older adults. Stakeholders from 318 health jurisdictions registered identified assets on the platform between July 2015 and December 2022. The agreement rate between the classification algorithm and verified data sets ranged from 62.02% to 99.47% across variables. Leisure and skill development activities were the most prevalent (1844/5022, 36.72%). Leisure and cultural associations, such as social clubs for older adults, were the most common resources (878/4524, 19.41%). Health asset registration varied across areas, ranging between 0 and 263 activities and 0 and 265 resources. Conclusions: The sequential use of WeTMS offers a robust method for generating data sets containing contextual-level variables from internet text data. This study can guide health and social researchers in efficiently generating ready-to-analyze data sets containing contextual variables.

The COVID-19 pandemic took most communities off guard and has highlighted gaps in community preparedness and resilience in spite of the numerous technological advancements and the variety of available social media platforms that many relied on during lockdown periods. This served to emphasise the necessity for exploring the roles of social media and smart city technologies in mitigating pandemic impacts. In this systematic literature review, we examined twelve articles on social media usage and smart city technologies and their contributions to community resilience during COVID-19. The analysis focused on the use of social media platforms and smart city technologies during and after lockdown periods, examining their role in fostering community resilience. Results indicate that social media and smart city technologies were instrumental in helping communities adapt and recover from the pandemic. While past studies have examined community resilience, social media, or smart cities separately, there is limited literature collating insights on the three elements combined. We therefore argue that these technologies, employed collaboratively, enhance community resilience during crises. Nevertheless, further research is recommended, particularly on urban resilience and comparative analyses to deepen our understanding of the complex interplay between these variables.

This paper explores the influence of social media in fostering resilience within an urban spatial context, specifically in Bangalore, India, during the COVID-19 lockdown, a period marked by a surge in digital communication due to movement restrictions. To control the rapid spread of the virus, over 1.38 billion people were given stay-at-home orders by the government of India during the onset of the pandemic. The restrictions in movement forced individuals to shift to online modes of connection and communication. As the field of digital epidemiology, that is, the use of digital tools and data to understand and improve health took center stage during the pandemic, the focus shifted towards the social media landscape, which is often associated with its negative aspects, such as misinformation. However, this paper delves into social media’s potential to build resilience on a local scale, particularly given its increased usage during the pandemic. Through in-depth online interviews with eight urban residents, we conducted a thematic analysis to understand social media’s role during the lockdown. Results indicate that social media facilitated effective information exchange and fostered a sense of community. Furthermore, it engendered an environment conducive to prosocial behavior, a known resilience amplifier. We also highlight the importance of baseline context regarding the users directly engaged in social media data generation with respect to digital epidemiology analytics tools for large-scale social media data and the need for qualitative input feeding into their design. Our study highlights the need for a balanced perspective on social media use in times of crisis, recognizing its potential to boost community resilience in an urban setting, and further enriching digital epidemiology approaches.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Contextual variables that capture the characteristics of delimited geographic or jurisdictional areas are vital for health and social research. However, obtaining data sets with contextual-level data can be challenging in the absence of monitoring systems or public census data. Objective: We describe and implement an 8-step method that combines web scraping, text mining, and spatial overlay analysis (WeTMS) to transform extensive text data from government websites into analyzable data sets containing contextual data for jurisdictional areas. Methods: This tutorial describes the method and provides resources for its application by health and social researchers. We used this method to create data sets of health assets aimed at enhancing older adults’ social connections (eg, activities and resources such as walking groups and senior clubs) across the 374 health jurisdictions in Catalonia from 2015 to 2022. These assets are registered on a web-based government platform by local stakeholders from various health and nonhealth organizations as part of a national public health program. Steps 1 to 3 involved defining the variables of interest, identifying data sources, and using Python to extract information from 50,000 websites linked to the platform. Steps 4 to 6 comprised preprocessing the scraped text, defining new variables to classify health assets based on social connection constructs, analyzing word frequencies in titles and descriptions of the assets, creating topic-specific dictionaries, implementing a rule-based classifier in R, and verifying the results. Steps 7 and 8 integrate the spatial overlay analysis to determine the geographic location of each asset. We conducted a descriptive analysis of the data sets to report the characteristics of the assets identified and the patterns of asset registrations across areas. Results: We identified and extracted data from 17,305 websites describing health assets. The titles and descriptions of the activities and resources contained 12,560 and 7301 unique words, respectively. After applying our classifier and spatial analysis algorithm, we generated 2 data sets containing 9546 health assets (5022 activities and 4524 resources) with the potential to enhance social connections among older adults. Stakeholders from 318 health jurisdictions registered identified assets on the platform between July 2015 and December 2022. The agreement rate between the classification algorithm and verified data sets ranged from 62.02% to 99.47% across variables. Leisure and skill development activities were the most prevalent (1844/5022, 36.72%). Leisure and cultural associations, such as social clubs for older adults, were the most common resources (878/4524, 19.41%). Health asset registration varied across areas, ranging between 0 and 263 activities and 0 and 265 resources. Conclusions: The sequential use of WeTMS offers a robust method for generating data sets containing contextual-level variables from internet text data. This study can guide health and social researchers in efficiently generating ready-to-analyze data sets containing contextual variables.

Background: In 2008, Ecuador introduced Plan Nacional para el Buen Vivir (PNBV; National Plan for Good Living), which was widely recognized as a promising example of Health in All Policies (HiAP) due to the integration of policy sectors on health and health equity objectives. PBNV was implemented through three successive plans (2009–2013, 2013–2017, 2017–2021). In a time of widening global health inequities, there is growing interest in understanding how politics and governance shape HiAP implementation. The objective of this study was to test specific hypotheses about how, why, to what extent, and under what circumstances HiAP was implemented in Ecuador. Methods: An explanatory case study approach (HiAP Analysis using Realist Methods on International Case Studies—HARMONICS) was used to understand the processes that hindered or facilitated HiAP implementation. Realist methods and systems theory were employed to test hypotheses through analysis of empirical and grey literature, and 19 key informant interviews. This case study focused on processes related to buy-in for a HiAP approach by diverse policy sectors, particularly in relation to the strong mandate and transformative governance approach that were introduced by then-President Rafael Correa’s administration to support PNBV. Results: The mandate and governance approach of the HiAP approach achieved buy-in for implementation across diverse sectors. Support for the hypotheses was found through direct evidence about buy-in for HiAP implementation by policy sectors; and indirect evidence about allocation of governmental resources for HiAP implementation. Key mechanisms identified included: influence of political elites; challenges in dealing with political opposition and ‘siloed’ ways of thinking; and the role of strategies and resources in motivating buy-in. Conclusion: In Ecuador, political elites were a catalyst for mechanisms that impacted buy-in and government funding for HiAP implementation. They raised awareness among policy sectors initially opposed to PNBV about the rationale for changing governance practices, and they provided financial resources to support efforts related to PNBV. Specific mechanisms help explain these phenomena further. Future studies should examine ways that PNBV may have been an impediment to health equity for some marginalized groups while strengthening HiAP implementation.

Background: Health in All Policies (HiAP) encompasses collaboration across government and the consideration of health in various governmental sector’s policies and decisions. Despite increasing advocacy, interest, and uptake in HiAP globally, empirical and evaluative studies are underrepresented in this growing literature, particularly literature on HiAP implementation at the local level. Finland has been a pioneer in and champion for HiAP. Methods: A realist explanatory case study design was used to test hypotheses about how HiAP is implemented in Kuopio, Finland. Semi-structured interviews with ten government employees from various sectors were conducted. Data from interviews and literature were analyzed with the aims of uncovering explanatory mechanisms in the form of context-strategy-mechanism-outcome (CSMO) configurations related to implementation strategies. Evidence was evaluated for quality based on triangulation of sources and strength of evidence. We hypothesized that having or creating a common goal between sectors and having committed staff and local leadership would facilitate implementation. Results: Strong evidence supports our hypothesis that having or creating a common goal can aid in positive implementation outcomes at the local level. Common goals can be created by the strategies of having a city mandate, engaging in cross-sectoral discussions, and/or by working together. Policy and political elite leadership led to HiAP implementation success because leaders supported HiAP work, thus providing justification for using time to work intersectorally. How and why the wellbeing committee facilitated implementation included by providing opportunities for discussion and learning, which led to understanding of how non-health decisions impact community wellbeing, and by acting as a conduit for the communication of wellbeing goals to government employees. Conclusion: At the municipal level, having or creating a common goal, leadership from policy and political elites, and the presence of committed staff can facilitate HiAP implementation. Inclusion of not only strategies for HiAP, but also the explanatory mechanisms, aids in elucidating how and why HiAP is successfully implemented in a local setting.

The physical and social characteristics of urban neighborhoods engender unique stressors and assets, contributing to community-level variation in health over the lifecourse. Actors such as city planners and community organizations can help strengthen resilience in places where chronic stress is endemic, by learning about perceived stressors and assets from neighborhood users themselves (residents, workers, business owners). This study piloted a methodology to identify Toronto neighborhoods experiencing chronic stress and to engage them to identify neighborhood stressors, assets, and solutions. Crescent Town was identified as one neighborhood of interest based on relatively high levels of emotional stress in Twitter Tweets produced over two one-year periods (2013–2014 and 2017–2018) and triangulation using other neighborhood-level data. Using concept mapping, community members (n = 23) created a ten-cluster concept map describing neighborhood stressors and assets, and identified two potential strategies, a Crescent Town Residents’ Association and a community fair to promote neighborhood resources and build social networks. We discuss how this knowledge has circulated through the City of Toronto and community-level organizations to date, and lessons for improving this methodology.