Mobilizing community health assets through intersectoral collaboration for social connection: Associations with social support and well-being in a nationwide population-based study in Catalonia
Bibliographic record
Abstract
BACKGROUND: Limited social connection among older adults poses a global public health challenge, reducing sources of support and affecting health and well-being. National public health strategies that leverage local intersectoral collaboration between key sectors such as primary and social care, community organizations, and society, have been advocated, yet their impact remains underexplored. OBJECTIVE: This study examines the regional variability in the uptake of a public health strategy in Catalonia that mobilizes community health assets, such as social clubs and leisure activities, through intersectoral collaboration and its associations with social support and mental well-being in older adults. METHODS: We conducted a population-based cross-sectional study using the Catalan Health Survey (2017-2021) with 6011 adults aged ≥ 60 years across 31 Health Sectors. Survey data were linked with area-level uptake metrics, generated using data analytic techniques. Individuals were categorized into three uptake groups based on the number and territorial distribution of asset-based initiatives within their area of residence. Multilevel regressions tested associations with social support (OSSS-3) and mental well-being (SWEMWBS), controlling for individual, contextual, and temporal factors. RESULTS: Participants' average age was 74.1 years ± 10.0 with 53.3% women. From 2017 to 2021, 2312 asset-based initiatives were registered across Health Sectors, ranging from 0 to 342 per sector. Residing in sectors with the highest uptake of initiatives (>15 initiatives per 10,000 population) was associated with higher social support (β = .34, p < .01) and mental well-being scores (β = 1.11, p < .01). CONCLUSION: Residing in areas with greater health assets mobilized through intersectoral collaboration was associated with higher social support and well-being among older adults. This study represents one of the first national evaluations of an intersectoral strategy aimed at mitigating the mental health impacts of limited social networks. Future public health strategies should prioritize equitable access for inclusive benefits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".