Introduction of a Patient as Teacher Program into family medicine residency: an exploratory pilot study
Bibliographic record
Abstract
Background: Family medicine residents should be prepared to address the psychosocial issues that breast cancer survivors may experience. Objectives: Our study aimed to implement a patient-centred approach model into the family medicine residency program and evaluate the impact of such a program on residents. Methods: An interactive virtual session (75 minutes), was integrated into the academic half-day of the family medicine residency program at St. Michael’s Hospital. The session was led by a cancer survivor and her partner. They discussed how illness has impacted their lives and reflect on their experiences with the health care system. The session was facilitated by a trained facilitator in health care. A qualitative approach was used to evaluate the impact of this program. Two focus groups for residents was conducted to evaluate the delivery mode, recommendations and impact of the proposed program. The focus group discussions were recorded, transcribed and thematically analyzed. Results: This program has had positive influences on residents by improving therapeutic relationships and enhancing the residents' understanding of the experience of illness. This program allowed residents to appreciate the importance of understating patients’ perspectives and values. Additionally, adding the partner perspective to the program was appreciated and valued by residents. Conclusions: Based on the school’s specific curriculum, this program can be integrated into the residents’ academic activities. This can improve important competencies for family medicine residents including confidence in communication and increased empathy. Family medicine residency programs wishing to enhance such humanism skills by family physicians might consider this model.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".