Finding the Balance: Uncovering Resilience in the Veterinary Literature
Bibliographic record
Abstract
Resilience is an issue of emerging importance in veterinary education and research, as in other professional contexts. The aim of this study was to perform an appraisal of how resilience is portrayed in the contemporary (1995-present) research and education literature around veterinary mental health, and to attempt a provisional synthesis informing a conception of resilience in the veterinary context. Qualitative analysis of the literature (59 sources included) revealed a dominant emphasis on mental health problems, particularly stress, which outweighs and potentially obscures complementary approaches to well-being and resilience. We found the construct of resilience underdeveloped in the veterinary literature and in need of further research, but provide a preliminary synthesis of key themes emerging from the current literature (emotional competence, motivation, personal resources, social support, organizational culture, life balance, and well-being strategies). We advocate for greater balance between complementary perspectives in veterinary mental health education and research, and propose that an increasing focus on resilience (here endorsed as a dynamic and multi-dimensional process involving personal and contextual resources, strategies, and outcomes) will help to address this balance.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".