Workplace Strategies to Reduce Burnout in Veterinary Nurses and Technicians: A Delphi Study
Bibliographic record
Abstract
Veterinary nurses and technicians are at risk of burnout, which negatively impacts mental and physical health, turnover, and patient care. Workplace contributors to burnout have been identified in this population, but little is known about best practice management strategies. This study used the Delphi method to explore barriers to addressing burnout and develop expert recommendations for workplace management strategies. Forty participants with a minimum of 5 years' industry experience in leadership, or wellbeing, were recruited via purposive sampling from the USA, UK, Australia, New Zealand, and Canada. Participants completed two anonymous, online, mixed-methods surveys between October 2024 and January 2025. Qualitative survey data were analysed using content analysis to identify codes and categorise solutions. Quantitative data were analysed using descriptive statistics. Barriers to addressing burnout included industry-wide barriers, such as lack of, or unclear, regulation and lack of leadership knowledge, and clinic-specific barriers, such as poor team culture, unwillingness for change, and existing burnout. Thirty-nine solutions were developed and rated as being highly, or very highly effective. These focused on themes such as improving communication, developing progression pathways, and providing leadership training and support. Existing workplace barriers must be evaluated prior to selecting strategies, to maximise effectiveness in specific contexts.
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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 | 0.032 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".