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Aims: This paper describes the use of three governance tools for health in all policies utilised to facilitate implementation in the municipality of Kuopio, Finland: impact assessments, a city mandate (the Kuopio strategy), and shared budgets. Methods: An explanatory case study was used. Data sources included semistructured interviews with 10 government employees and scholarly literature. Realist scientific methods were used to reveal mechanisms underlying the use of tools in health in all policies. Results: Strong evidence was found supporting initial and new theory/hypotheses regarding the use of each tool in achieving positive implementation outcomes. Impact assessments facilitated health in all policies by enhancing understanding of health implications. The Kuopio strategy aided in implementation by giving credence to health in all policies work via formal authority. Shared budgets promoted intersectoral discussions and understanding, and a sense of ownership, in addition to allowing time to be spent on health in all policies work and not financial deliberation. Conclusions: Findings confirm the efficacious use of three governance tools in implementing health in all policies in Kuopio. Knowledge and evidence-based guidelines on local health in all policies implementation are needed as this policy approach continues to be recognised and adopted as a means to promote population health and health equity. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the association between dentists' geographic density and perceptions of market competition with clinical decision‐making among a representative sample of dentists in Ontario, Canada's most populated province. Methods: Competition was quantified using dentist density, defined as the number of dental clinics lying within a one kilometre radius around the respondents' clinic address and by self‐reported perceived pressure from other dental clinics. The outcome (clinical decision‐making or treatment intensity) was measured using a set of clinical scenarios, which categorized dentists as either relatively aggressive or conservative. Associations were assessed using bivariate analysis and logistic and linear regression. Results: Dentists who perceived large competitive pressure from other dentists (OR = 1.63, 95% CI: 1.07‐2.49) were relatively more aggressive in their treatment choices. Interestingly, dentists located in very low dentist density areas (OR = 1.31, 95% CI: 1.03‐1.68) were also relatively more aggressive in their treatment choices. Conclusion: This study is the first to explore the impact of competition on the clinical decision‐making of dentists in a Canadian context. It presents a valuable addition to the competition literature and helps to understand current dynamics in the Canadian dental care market. [ABSTRACT FROM 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., (©Pablo Galvez-Hernandez, Angelina Gonzalez-Viana, Luis Gonzalez-de Paz, Ketan Shankardass, Carles Muntaner. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 08.01.2024.)