Health promotion in primary care across Brazil: Genealogic-inspired qualitative study
Bibliographic record
Abstract
Objective: To describe and analyze Health Promotion practices in Primary Health Care. Method: Genealogic-inspired qualitative, descriptive, and exploratory research conducted in a PHC service in Porto Alegre, Rio Grande do Sul, Brazil. Twenty-three semi-structured interviews were held with the service staff from February to May 2020. The data were qualitatively analyzed using genealogically inspired techniques, which allowed us to identify tensions, disputes, discourses, practices, and power relationships. Results: We established eight sets of Health Promotion practices: 1) Educational activities focused on behavioral/habit changes and development of personal abilities; 2) Intersectoral practices and community social networks involving other community equipment; 3) Practices that encourage community organization and participation; 4) Integrative and Complementary Health practices; 5) Practices that stimulate meeting people, sociability, art, and creativity; 6) Practices that encourage environmental and food sustainability; 7) Practices that stimulate income generation; 8) Community communication practices. Conclusion: We identified a heterogeneous field of practices to promote health established through the circulation of different types of knowledge and powers. The practices are permeated by discourses linked to neoliberal governability and practices that position themselves against such discourse.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".