Identifying the mentorship needs among faculty in a large department of psychiatry- support for the creation of a formal mentorship program
Bibliographic record
Abstract
Abstract Background Study aims were to assess the current state and needs of faculty to inform the design of a formal mentorship program in a large academic Department of Psychiatry. Methods A 57- item self-administered online survey questionnaire was distributed to all faculty members. Results 225 faculty members completed the survey (24%). 68% of respondents had a mentor and reported high satisfaction (mean = 4.3, SD = 1.05) (range 1 to 5). Among those respondents lacking access to mentorship, 65% expressed interest. Open-ended questions indicated that international medical graduates, faculty identifying as minority, women and clinician teachers may lack access to mentorship. PhD faculty felt disadvantaged compared to MD faculty in gaining first authorship ( M Non−MD =1.64 ± 0.79 vs. M MD =1.36 ± 0.67; t = 2.51, p = .013); reported more authorship disputes ( M Non−MD =1.99 ± 0.91 vs. M MD =1.66 ± 0.76; t = 2.63 p = .009) and experienced questionable scientific integrity concerning colleagues ( M Non−MD =2.01 ± 0.92 vs. M MD =1.70 ± 0.81; t = 2.42 p = .017). For both MD and PhD faculty, women were significantly more likely to experience authorship disputes (χ 2 (2) = 8.67, p = .013). The department was perceived as treating faculty with respect (72% agreed) with 54% agreeing that it embraces diversity (54%). Identified benefits to mentorship included receiving advice about academic promotion, opportunities for career advancement, advocacy, and advice as a researcher, teacher or clinician. Only 26% of mentors received formal training to support their role; 59% expressed interest in education. Respondents supported a more formal, accessible, inclusive program, with training, tools, and a matching strategy based on mentee preferences. Conclusions Challenges and inequities were identified with the department’s current ad hoc approach to mentorship. A limitation of the study was the response rate, while similar to response rates of other physician surveys, raises the potential for response bias. In comparing study participants to the department, the sample appeared to provide a fair representation. The study has implications for identifying the need and design of more formal mentorship programs in academic medicine.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".