Investing in the Future: A Comprehensive Evaluation of Mentorship Networks for Residents
Bibliographic record
Abstract
Background Mentorship plays a key career development role in medicine. Traditional mentorship consists of dyadic relationships between mentors and their mentees. However, research favours utilization of mentorship networks involving individuals at multiple levels. Objective This study aimed to rigorously evaluate a formalized mentorship network program within a Canadian Internal Medicine residency program from 2012 to 2013. Methods Residents participated in one-on-one semi-structured interviews at baseline and after one year of participation in the mentorship network. Closed-ended surveys assessed affective organizational commitment, self-efficacy, career satisfaction and overall wellness among residents and faculty members. 89 residents and 28 faculty members were invited to participate; 40 residents and 18 faculty members completed the survey after one year. Results Residents perceived mentorship networks to add value across multiple domains, including self-awareness, overall efficiency, and physician wellness. Satisfaction with the program was very high, with 98% ( n = 39/40) of residents and 89% of faculty members ( n = 16/18) wanting the program to continue after year one. Male mentors were more likely to report benefits from serving as a mentor than their female counterparts. In contrast to this, female mentees found mentorship more useful than male mentees. Conclusions Network mentorship is associated with personal and system benefits, though these benefits are difficult to quantify. The network model is feasible and well-received by mentors and mentees. Further research considering both short- and long-term endpoints is required to delineate the true cost-benefit ratio of mentorship programs to both mentors and mentees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".