Authentic Conversations about Self-Care with Fourth-Year Veterinary Medical Students
Bibliographic record
Abstract
Expanding literature on well-being within veterinary medicine has been instrumental in recognizing the prevalence of psychological distress among students and practitioners and promoting awareness and advocacy for well-being within teaching institutions, professional associations, and the workplace. However, greater focus on distress has also been critiqued for overemphasizing illness and reactive interventions, and a call has been made for more balanced conversations about veterinary well-being, with focus on strengths and proactive interventions. This Best Practices article highlights a proactive, strengths-focused intervention, aimed at increasing fourth-year students' awareness of self-care while in training and in their transition to the profession. Authentic conversations about self-care is a required part of clinical training at Kansas State University College of Veterinary Medicine. All students attend a private meeting with a behavioral scientist, engaging in an authentic conversation about their experience of stress and coping strategies. Current practices in providing stress management interventions are restricted to pre-clinical training. Authentic conversations about self-care are presented here as an alternative to current practices, which the authors argue are not adequately addressing students' needs during clinical training and the transition to the profession. Potential implications of providing self-care interventions during the clinical year of training include greater student engagement, increasing awareness, and self-efficacy as students make efforts to maintain well-being both in training and in the professional realm.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".