Rewards and recognition for Canadian distributed medical education preceptors: a qualitative analysis
Bibliographic record
Abstract
<ns3:p> <ns3:bold>Background</ns3:bold> : Recognition of Distributed Medical Education (DME) preceptors by medical institutions ensures that important community-based training opportunities remain available to learners. Yet the literature seldom explores what rewards are meaningful to this population of teachers. The goal of our national project was to provide guidance to medical schools about the financial remuneration and non-financial rewards that are most valued by DME preceptors. </ns3:p> <ns3:p/> <ns3:p> <ns3:bold>Methods</ns3:bold> : In this qualitative study, we invited DME faculty members from all Canadian medical schools to participate in semi-structured interviews. Participants with a range of medical specialties, stages of career, and geographic locations were interviewed via Zoom videoconferencing. The sessions in English and French were audio-recorded and transcribed. We used line-by-line inductive coding and thematic analysis to examine participant talk about meaningful preceptor recognition. </ns3:p> <ns3:p/> <ns3:p> <ns3:bold>Results</ns3:bold> : Fourteen participants from multiple provinces were interviewed. Results indicated that the DME faculty are a diverse group of people with diverse needs. Most of the interviewees appreciated the rewards and recognition provided by their institutions but felt that there are areas for improvement. Recognition is not necessarily monetary and should be tailored to the needs and the values of the recipient. Other themes included: benefits and challenges of being a preceptor, current institutional structures and supports, and the impact of the pandemic on preceptors. </ns3:p> <ns3:p/> <ns3:p> <ns3:bold>Conclusions</ns3:bold> : The interviews highlighted the importance placed by preceptors on personal rewards and a wide variety of forms of recognition. Based on the findings, we suggest specific steps that medical institutions can take to support, engage, and recognize DME faculty. </ns3:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".