University of Ottawa Vertical Mentorship Program: Improving Engagement through Simple Innovation
Bibliographic record
Abstract
Objective: To assess the impact of simple innovations on three identified program gaps (attendance and engagement, understanding of the program, issue identification and resolution).Methods: Survey responses, event attendance, and subjective observations, were compared between the 2014 - 2015 and 2015 - 2016 academic years, providing direct and indirect measures of gap closure.Results: Attendance and engagement - Mid-year survey response rate was excellent (n=133), and increased responses from second year coordinators and mentors were seen in both 2015-2016 surveys’. Dessert night attendance increased from 2014 to 2015 (383 to 436). End of year event numbers decreased year-to-year (163 to 115). Only 5.9% of students did not attend events due to a lack of interest in the program.Issue identification and resolution - Mid-year surveys identified three groups with difficulties communicating. Knowledge of available resources in mentors rose by 5% between years, and by 55% in second year coordinators.Understanding of the program - 12 of the 52 mentorship groups actively used Facebook to engage and plan joint activities. The nominations received at the end of the year, and survey comments, focused beyond topics of career mentoring, and expanded to the impact of the field on life, life in medicine, and family.Conclusion: Identifying gaps in a program such as the VMP, through eliciting feedback from those participating may be challenging. Establishing simple innovations may be an effective way to improve participants’ experiences and overall functioning of the program.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".