Implementation of a university faculty mentorship program
Bibliographic record
Abstract
OBJECTIVE: To implement a University Faculty mentorship program in the Division of Emergency Medicine. METHODS: A program based on a unique Schulich faculty mentorship policy was implemented with the help of a Provider Value Officer. The process involved creating a training program which defined the roles of the mentors and mentees and established the principles of an effective mentor-mentee relationship. Faculty received training on how to participate effectively in a Schulich faculty mentorship committee. Each committee consisted of a mentee, and two mentors at the associate professor level (one internal and one external). Thirteen distinct external divisions were represented. They were instructed to meet twice per year, as arranged by the mentee. The mentee created mentor minutes using a template, and then submitted the minutes to the members of the mentorship committee and the Chair/Chief of Emergency medicine. The Chair/Chief used the minutes during the annual Continuing Professional Development meeting. RESULTS: In less than a year, the division has successfully transformed its mentorship program. Using the above-mentioned process, 31 of 34 (91%) eligible assistant professors have functioning mentorship committees. Collaboration and participation between the different faculties has increased. Follow-up meetings with the Chair/Chief and the Provider Value Officer revealed the theme that, universally, participants have perceived Schulich Faculty Mentorship committees as beneficial and are happy with the "fit" of their mentorship committees. CONCLUSION: Through careful planning and training, a successful Faculty Mentorship program can be initiated in an academic division in less than a year with the help of a local champion given protected time.
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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 | 0.048 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".