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 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 | 0.028 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".