<i>Promoting Healthy Lifestyles</i> In Ontario Family Health Networks
Bibliographic record
Abstract
PURPOSE: Primary health care reform presents new opportunities for registered dietitians (RDs) to contribute to health promotion and disease prevention in family practices. Since this is an emerging area of RD practice, a health promotion specialist was contracted to conduct a needs assessment and develop a plan for implementing nutrition-focused healthy lifestyle activities. METHODS: The needs assessment was conducted as part of an Ontario-based demonstration project in three Family Health Networks (FHNs). RESULTS: The needs assessment revealed a lack of agreement about what types of activities should be undertaken, a lack of information on the population's needs, a lack of coordination with other agencies in the community, and barriers of time and resources. The health promotion specialist recommended that health care team members in each FHN develop a shared understanding of their goals, and undertake the entire planning and evaluation cycle. Specific strategies were suggested to increase awareness, to provide health education, and to improve environmental support. CONCLUSIONS: A significant need exists for conceptual development, planning, testing, and evaluation of disease prevention and health promotion in family physician-based primary health care organizations. The findings may be useful to others interested in increasing the focus on health promotion and disease prevention in such 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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".