Special Features: Health Policy: Health Promotion and Disease Prevention Among Nurses Working in Local Public Health Organizations in Montréal, Québec
Bibliographic record
Abstract
OBJECTIVE: This study investigates conceptualizations of disease prevention and health promotion (DPHP) among nurses from local public health organizations in Montréal, Québec. DESIGN AND SAMPLE: A collaborative qualitative study was conducted among a purposive sample of 41 nurses. MEASURES: Semi-structured interviews investigated two themes: meanings attributed to DPHP as well as nurses' recent DPHP activities. RESULTS: Although the meaning attributed to prevention referred to standard definitions, health promotion was often defined as large-scale health education oriented toward the attainment of positive results, such as health and well-being. Almost completely absent from participants' discourse were central notions such as empowerment and health determinants, including socioenvironmental dimensions of health. With regard to activity descriptions, there was a very partial coverage of the full spectrum of DPHP. Participants rarely went beyond traditional health education activities aimed at an individual target. Finally, a sizeable number of participants appeared to be unable to provide a clear distinction between the terms "health promotion" and "prevention." CONCLUSIONS: The results are consistent with a conclusion frequently drawn by commentators and researchers alike that highlight a narrow range of DPHP nursing practices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".