Patient trainer in postgraduate family medicine training: A quality improvement initiative
Bibliographic record
Abstract
Context: In Quebec, the mission of the university family medicine groups (U-FMG) is to train family medicine residents (FMRs) to provide exemplary, interprofessional collaborative, integrated front-line health care and services. FMRs participate in Practice-Based Learning Program (PBLP) workshops as part of their clinical training. PBLP workshops enable FMRs to learn in small groups, facilitated by a team of teaching professionals. In August 2022, the U-FMG Notre-Dame attempted to innovate its teaching by engaging a patient trainer (PT) in a PBLP workshop to improve the quality of FMRs’ PBLP training on chronic obstructive pulmonary disease and smoking cessation. Objective: Exploring the perspective of FMRs on the involvement of a PT in the PBLP offered in U-FMG. Study Design and Analysis: Explanatory Sequential Design. Firstly, the experience of the FMRs was explored using a questionnaire developed and validated. Subsequently, a focus group was conducted to deepen the results obtained in the quantitative phase. Setting or Dataset: Quantitative data were analyzed using descriptive statistics, while qualitative data were analyzed using combined deductive-inductive content analysis. Population studied: The study involved first- and second-year FMRs who had completed their doctorate in Quebec. Intervention: Involvement of a PT in a 3-hour PBLP workshop on chronic obstructive pulmonary disease (COPD) and smoking cessation, after prior preparation of the PT with the PBLP workshop’s teaching professionals. Outcome Measures: FMRs’ perspective of PT utilities to their training. Results: All FMRs (n=16) completed the questionnaire, and 4 FMRs participated in the focus group. Most FMRs mentioned improving their knowledge of patient-partnered care after the workshop, except for improving their understanding of patients’ rights. The main issues reported by the FMRs concerned the lack of preparation on the part of the PT and the FMRs, their difficulties in communicating points of view different from those of the patient, and in fully understanding the role of the PT and recognizing his experiential knowledge. Conclusions: The contribution of PT to the training of FMRs is promising and could be evaluated more extensively to improve the quality of training. FMRs suggested ways to improve the contribution of PT to training.
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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 | 0.061 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".