223 Implementation of a community ‘Rallye Ressources’ activity for family medicine students
Bibliographic record
Abstract
Introduction Family medicine principles include responsibility for a community. We aim to implement an educational activity, the ‘Rallye Ressources’ (RR), which brings family medicine trainees to discover community- based organizations and explore collaboration with them in one Faculty of Medicine in France. Methods Informed by the CARDA reporting guidelines, we will conduct a descriptive study to report on the organization and co-development of the RR, held annually in several family medicine groups of Université Laval and to be implemented in one Faculty of Medicine in France. The activity is designed to teach family medicine trainees the community sociodemographics within which they train and have them reflect on their responsibilities toward this community. Residents visit community organizations over the course of a day and have discussion with community-based agents. Data collection includes official documents from the organizations and the family medicine training program and interviews with 5 to 13 health professionals who organize the activities and self-administered questionnaires to 5–10 community-based agents to learn about barriers and enablers of the RR implementation. In France, we will be co-developing an adapted version of the RR with stakeholders. A pilot RR will be carried out and assessed in participating residents with self-administered questionnaires. Preliminary Results The expected results are a better understanding of the organization and implementation process of the RR. Already, 3 participants have agreed to be interviewed and 5 have provided us with documents. Discussion Co-development is an important part of this study as it ensures that the activity will be adapted to the local context. Conclusion This study will provide a better understanding of how to improve training of family medicine residents in community dimensions.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".