Family medicine residents’ perspectives about patient partners in teaching participation in their training: A retrospective case study using a mixed-method explanatory sequential design
Bibliographic record
Abstract
Abstract Objective: To explore the perspective of family medicine residents (FMRs) about patient partners in teaching participation in the practice-based learning program (PBLP) offered in university family medicine groups (U-FMG). Participants and methods: The study was carried out among first- and second-year FMRs who completed their doctorate/externship in Quebec and attended the PBLP workshop involving a patient partner in teaching from U-FMG Notre-Dame. FMRs completed a questionnaire at the end of the PBLP workshop, and quantitative data were analyzed descriptively. Then, a focus group was conducted with some of these FRMs. The results were analyzed by two co-coders using DedooseÒ software. Results: All FRMs (n=16) completed the questionnaire, and 4 FRMs participated in the focus group. The majority of FRMs mentioned having improved their knowledge of care offered in partnership with patients after the workshop but not their understanding of patients' rights. Two major themes emerged from the analysis: 1) knowledge and skills sought and 2) factors influencing the partnership with the patient partner in teaching. Conclusion: The contribution of patient partners in teaching to the training of FRMs is promising and could be evaluated more extensively to improve the quality of training. The FRMs raised several avenues for improvement.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".