Mentoring matters to early‐career veterinarians' confidence, clinical growth and career retention: A qualitative interview study
Bibliographic record
Abstract
BACKGROUND: Veterinarians in clinical practice, especially early-career veterinarians and those identifying as women, have higher levels of negative mental health outcomes compared to the general population. Partnering with a mentor can mitigate some of the work-related challenges that may contribute to poor mental health and career attrition. Our objectives were to understand: (1) mentee veterinarians' expectations and experiences in mentorship relationships, (2) how gender identity affects mentees' expectations and experiences, and (3) the role of mentorship in mental wellbeing and career retention. METHODS: Semi-structured online interviews with 17 gender-diverse early- to mid-career veterinarians from various regions of Canada who had experience as mentees were analysed using template analysis. RESULTS: Participants described that the 'safety net' (support with clinical decision making) mentors provided helped mitigate stressful early-career experiences and aided in retention. Most participants supported a formal, more structured and consistent mentorship practice. Women in this study more commonly valued psychosocial support and advice on work‒life balance and family challenges than participating men. Mentoring styles were identified as making or breaking the mentorship experience, and many participants described complex interpersonal dynamics, including mentor resentment and competition. LIMITATIONS: Qualitative research is not generalisable, and should be considered accounting for the research context, methodology, and authors' positionality. CONCLUSION: This study highlights the importance of mentorship for early-career veterinarians' confidence, clinical growth, wellbeing and career retention, while taking into account how mentorship is delivered and by whom.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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 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".