Promoting Health in a Rural Community in the Basque Country by Leveraging Health Assets Identified through a Community Health Diagnosis
Bibliographic record
Abstract
Salutogenesis focuses on factors that generate health and is a useful construct for identifying factors that promote health and for guiding activities to this end. This article describes health assets identified in a community diagnosis and how to leverage them with actions for improvement to deepen the understanding of this concept and its impact on health promotion. An intervention strategy was designed following the principles of participatory action research (PAR). The study was carried out in Mañaria (Basque Country, Spain) using semi-structured and in-depth interviews, participant observation, desk review, and photographs, alongside different participatory strategies. Twenty-six women were interviewed, 21 of whom were community inhabitants, and five were key informants who worked in public or private institutions. Participant recruitment stopped when data saturation was reached. Data were analysed through discourse analysis, progressive coding, and categorisation. Six meta-categories emerged, and for each of these categories, health assets were identified together with actions to improve the community’s health. The latter were presented by the community to the authorities to trigger specific actions towards improving the health of the community. Identification of health assets led to different actions to improve the health of the community including improving the existing physical and social environments, personal and group skills, and the promotion of physical, social, emotional and cultural well-being.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".