Engaging Retired Physicians as Educators: Motivations and Experiences of Participants in a Novel Educational Program
Bibliographic record
Abstract
PURPOSE: Physician retirement has important impacts on medical learners as well as retiring physicians themselves. Retiring physicians take with them a wealth of knowledge, wisdom, and expertise and can feel a loss of identity, lack of fulfillment, and reduced social connectedness after leaving the institution. To address this, a novel educational program providing retired physicians with renewed educational roles was implemented in 2018 within a university-associated pediatric department. This study sought to explore the retired physicians' experiences in this new intergenerational program, including their motivations to reengage as educators after retirement. METHOD: The authors designed this study using qualitative description. Semistructured interviews were conducted in the Department of Pediatrics of McGill University in 2019 with retired physicians who participated in the educational program's inaugural year. Role theory and psychosocial development theory were used to design the interview guide and inform the thematic analysis. Iterative analysis of the interview transcripts was deductive and inductive. RESULTS: Of the 8 retired physicians who participated in the program's first cohort, 7 participated in this study. Analysis of the data yielded 4 main themes: a challenging shift to retirement, a desire for reengagement after retirement, role dissonance, and gaining by giving. The retired physicians were motivated to engage as educators. Although they experienced some discomfort in their new nonclinical roles, they described their experiences as fulfilling, with benefits such as intellectual stimulation, social connectedness, and a sense of purpose. CONCLUSIONS: Retired physicians' motivations to reengage academically and their experiences contributing to educational activities in this program highlight the importance of supporting physicians during the transition to retirement and establishing formal programs to engage retired physicians as educators.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".