Family Medicine Supervisors’ Preferences for Improving Their Teaching Skills in Senior Care
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Many clinical supervisors in family medicine feel ill-equipped to teach senior care to their family medicine residents (trainees). We therefore sought to explore their preferred learning strategies for improving their clinical and teaching skills with regard to senior care. METHODS: In this qualitative study, we conducted focus groups and interviews with supervisors from four family medicine clinics, to explore their preferred educational strategies. We selected four clinics using a maximum-variation strategy, based on a survey assessing continuing professional development (CPD) needs. The qualitative thematic analysis followed an inductive/deductive approach based on McGuire's attributes of persuasive communication. RESULTS: The four focus groups and nine interviews with 53 supervisors (37 physicians, 9 nurses, 4 psychologists, 1 social worker, 1 nutritionist, 1 sexologist) revealed that supervisors preferred being trained by experienced trainers specialized in senior care, from various professional backgrounds, and knowledgeable about local community resources. They valued practical training the most, such as clinical case discussions based on real cases, clinical tools, and mentoring. The findings also suggest that training in senior care should be adapted to the supervisors' experience, profession, workload, and scope of intervention. Supervisors valued repeated CPD with longitudinal follow-up and easy access to trainers and to up-to-date training content. CONCLUSIONS: The findings of this project will allow those who design CPD activities to adapt such activities to the preferences of supervisors, so as to improve their clinical and teaching skills in senior care. This, in turn, may help supervisors to embody an appealing professional role model for learners.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".