Veterinarians navigating and overcoming challenges in clinical practice: a collaborative narrative inquiry
Bibliographic record
Abstract
Veterinarians experience significant mental health challenges, and much of the research on veterinarians’ mental health has focused on poor mental health outcomes rather than veterinarians’ resilience or strengths. This study explored the process of overcoming challenges in clinical practice. Individual interviews were conducted with 11 early to mid-career veterinarians in Prince Edward Island, Canada, who provided a total of 30 stories of overcoming a difficult event or hardship. We used a collaborative, structural, narrative inquiry methodology to refine and analyse the data through narrative reconstructions, follow-up interviews, and observations. Five macro-threads were identified in analysis: (1) internal strengths, (2) healing through human connection, (3) helping myself change for the better, (4) reconnecting with meaning as a veterinarian, and (5) faith and optimism. The findings highlighted elements of the process of overcoming challenging experiences in clinical practice that allowed participants to stay in their career, such as developing one’s identity, a belief in oneself, meaning, and relationships. The insights derived from this narrative inquiry have implications for veterinary education and clinical veterinarians, including the importance of providing environments and teaching skills that allow for a broader perspective of wellbeing, rather than a narrow focus on identifying stressors or improving technical skills.
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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 | 0.033 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".