Mentoring needs of distributed medical education faculty at a Canadian medical school: a mixed-methods descriptive study.
Bibliographic record
Abstract
INTRODUCTION: The Schulich School of Medicine & Dentistry in London, Ontario, has a mentorship program for all full-time faculty. The school would like to expand its outreach to physician faculty located in distributed medical education sites. The purpose of this study was to determine what, if any, mentorship distributed physician faculty currently have, to gauge their interest in expanding the mentorship program to distributed physician faculty and to determine their vision of the most appropriate design of a mentorship program that would address their needs. METHODS: We conducted a mixed-methods study. The quantitative phase consisted of surveys sent to all distributed faculty members that elicited information on basic demographic characteristics and mentorship experiences/needs. The qualitative phase consisted of 4 focus groups of distributed faculty administered in 2 large and 2 small centres in both regions of the school's distributed education network: Sarnia, Leamington, Stratford and Hanover. Interviews were 90 minutes long and involved standardized semistructured questions. RESULTS: Of the 678 surveys sent, 210 (31.0%) were returned. Most respondents (136 [64.8%]) were men, and almost half (96 [45.7%]) were family physicians. Most respondents (197 [93.8%]) were not formal mentors to Schulich faculty, and 178 (84.8%) were not currently being formally mentored. Qualitative analysis suggested that many respondents were involved in informal mentoring. In addition, about half of the respondents (96 [45.7%]) wished to be formally mentored in the future, but they may be inhibited owing to time constraints and geographical isolation. Consistently, respondents wished to have mentoring by a colleague in a similar practice, with the most practical being one-on-one mentoring. CONCLUSION: Our analysis suggests that the school's current formal mentoring program may not be applicable and will require modification to address the needs of distributed faculty.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".