Evaluation of a collaborative mentorship program in a multi-site postgraduate training program
Bibliographic record
Abstract
BACKGROUND: Traditional one-on-one mentorship of trainees is challenging for multi-site training programs. Our three-site Neonatal - Perinatal Medicine Training Program therefore implemented collaborative mentorship. AIM: To describe and evaluate the effectiveness of collaborative mentorship. METHOD: Faculty Advisory Committee Triads (FACTs), comprising one staff neonatologist from each site, were created for each trainee. Guidelines for meeting frequency and process were developed. After 3 years, participants were invited to complete a questionnaire exploring three domains - helpfulness, participant opinion, and process. RESULTS: Twenty-four staff participated in 32 FACTs that mentored 32 trainees; 19 staff (79%) and 19 trainees (60%) completed the survey. All but one respondent preferred FACTs to individual mentors. Trainees were comfortable discussing both training program issues (90%) and social or personal issues (47%) with their FACT. Despite various ethno-cultural backgrounds, only 26% thought these should be similar for FACTs and trainees. More than 80% found FACTs supportive and beneficial for providing staff contacts at each site. Trainees found FACTs helpful for career planning, resource identification, clinical performance advice, and research motivation. More staff (79%) than trainees (33%) felt FACTs helped trainees get started in the program (p = 0.01), perhaps because not all trainees (47%) met with their FACT at the start of training. FACTs met one to four times annually; staff availability made scheduling difficult. CONCLUSION: In a multi-site training program, collaborative mentorship was effective in overcoming many barriers encountered with one-on-one mentorship.
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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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".