160: Peer Group Mentoring of Faculty in a Distributed Medical Education Setting
Bibliographic record
Abstract
Medical education is now being delivered in distributed medical education settings in many medical schools in Canada. There are scant financial and manpower resources. Foundational components necessary for faculty development have not been formally established. There was a need to implement a mentorship program to enhance faculty development. The pediatrics department in one such setting undertook a peer group mentoring pilot program to address these needs. The objective of this project was to explore the development of mentoring using a peer group model, and evaluate the benefits of such a model on faculty satisfaction, work-life balance, program and process development, personal and faculty development, as well as research gains. Adjunct faculty in the Department of Pediatrics were asked to be part of a pilot mentor-ship project for faculty in a distributed medical education setting. Research ethics approval was obtained. A collaborative and mixed methods approach was taken. The participants were interviewed at the end of the series of mentorship seminars. Evaluation forms were also collected at the end of each seminar. The group decided on a number of faculty development topics that were relevant to them. The framework for selection of topics was the CanMEDS non-expert roles. The seminars were presented at a regularly scheduled journal club. Participants: 6 to 11; Years in pediatric practice: 17 to 26 years; Number of faculty: 15; M:F – 2:1 The participants felt that the sessions were very helpful and increased collegiality. They remarked that they felt valued and that they were part of selecting the topics for peer group mentoring. Evaluation forms revealed moderate to high scores. This peer mentoring model may provide the stepping stone or the scaffolding on which to build a more conventional mentoring relationship. Benchmarks for the number of faculty achieving assistant professor status in the next five years should be made for those departments who adopt a peer mentoring program. More engagement and willingness to teach medical students and residents; increase research activities; engagement in hospital or medical school leadership; and higher medical student achievement in pediatrics could also be evaluated in the future. There is a lack of funding for this type of research. This pilot project adds to the scant body of work that supports peer or collaborative mentoring as an important pathway to achieving personal, institutional and societal goals.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".