Health promotion activity in primary care: performance of models and associated factors.
Bibliographic record
Abstract
BACKGROUND: Lifestyle behaviours have significant health and economic consequences. Primary care providers play an important role in promoting healthy behaviours. We compared the performance of primary care models in delivering health promotion and identified practice factors associated with its delivery. METHODS: Surveys were conducted in 137 randomly selected primary care practices in 4 primary care models in Ontario, Canada: 35 community health centres, 35 fee-for-service practices, 35 family health networks and 32 health service organizations. A total of 4861 adult patients who were visiting their family practice participated in the study. Qualitative nested case studies were also conducted at 2 practices per model. A 7-item question was used to evaluate health promotion. The main outcome was whether at least 1 of the 7 health promotion items was discussed at the survey visit. Multilevel logistic regressions were used to compare the models and determine performance-related practice factors. RESULTS: The rate of health promotion was significantly higher in community health centres than in the other models (the unadjusted difference ranged between 8% and 13%). This finding persisted after controlling for patient and family physician profiles. Factors independently positively associated with health promotion were as follows: reason for visit (for a general checkup: adjusted odds ratio [AOR] 3.34, 95% confidence interval [CI] 2.81-3.97; for care for a chronic disease: AOR 2.03, 95% CI 1.69-2.43), patients having and seeing their own provider (for those not: AOR 0.58, 95% CI 0.43-0.78), number of nurses in the practice (AOR 1.07, 95% CI 1.02-1.12), percentage of female family physicians (AOR 1.38, 95% CI 1.15-1.66), smaller physician panel size (AOR 0.92, 95% CI 0.85-1.01) and longer booking interval (AOR 1.03, 95% CI 1.01-1.04). Providers in interdisciplinary practices viewed health promotion as an integral part of primary care, whereas other providers emphasized the role of relational continuity in effective health promotion. CONCLUSION: We have identified several attributes associated with health promotion delivery. These results may assist practice managers and policy-makers in modifying practice attributes to improve health promotion in primary care.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".