Enhancing Mentorship in Psychiatry and Health Sciences: A Study Investigating Needs and Preferences in the Development of a Mentoring Program
Bibliographic record
Abstract
Preferences for the delivery of department-led mentorship programs are important to understanding and closing the gap between mentorship need and mentorship actualization. The objective of this paper is to, therefore, describe the perceived needs and barriers to mentorship in a postgraduate psychiatry program through separate mixed-methods surveys for psychiatry residents and health sciences faculty at a Canadian University. The surveys explored (1) the prevalence of mentorship, (2) barriers to adequate mentorship, and (3) program initiatives that could address these barriers. Qualitative responses were analyzed using an inductive analytic approach. The results of both surveys revealed that while psychiatry residents and faculty believed mentorship to be important for career success, fewer than half of residents (33%) or faculty (47%) reported receiving mentorship in the department. Residents and faculty ranked lack of exposure to mentorship, and lack of time as their top barrier to mentorship, respectively. The following components of a mentorship program were described as ideal: (1) the ability to choose one's own mentor, (2) training sessions for mentors, and (3) faculty mentoring webpage profiles to facilitate the matching of interests. Respondents suggested that mentoring program developers should foster a culture encouraging mentorship, seek mentors outside of regular program-related supervision, allow mentees to choose a mentor, and establishing structure, through aligning expectations and goal setting in mentoring relationships. There is a gap between desire for mentorship and actualization. Program developers in psychiatry medical education may choose to incorporate these findings to enhance mentorship.
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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".