Bridging the gap in population health for rural and Aboriginal communities: a needs assessment of public health training for rural primary care physicians.
Bibliographic record
Abstract
INTRODUCTION: The literature identifies significant inequalities in the health status of rural and Aboriginal populations, compared with the general population. Providing rural primary care physicians with public health skills could help address this issue since the patterns of mortality and morbidity suggest that prevention and health promotion play an important role. However, we were unable to identify any community needs assessment for such professionals with dual skills that had been performed in Canada. METHODS: We conducted key informant interviews and focus groups in 3 rural and Aboriginal communities in British Columbia (chosen through purposive sampling). We analyzed transcripts following standard qualitative iterative methodologies to extract themes and for discussing content. RESULTS: There was broad support for a program to train primary care physicians in public health. The characteristics identified as necessary in such a physician included a long-term commitment to the community with partnership building, advocacy, communication and cultural sensitivity skills. The communities we studied identified some priority challenges, most notably that the current remuneration structure does not support physicians engaging in public health or research. CONCLUSION: There is great potential and support for the training of rural primary care practitioners in public health to improve population health and engage communities in this process.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".