Latent burnout profiles of veterinarians in Canada: Findings from a cross‐sectional study
Bibliographic record
Abstract
BACKGROUND: Although burnout is often discussed as 'present' or 'not-present', the conceptual framework of an engagement-burnout continuum is more accurate and useful. Recognition of individuals' transitional states of burnout also allows for earlier detection of issues and tailored interventions to address the full burnout spectrum. METHODS: Previously reported Maslach Burnout Inventory-Human Services Scale (MBI-HSS) data from a 2017 national survey of 1272 veterinarians across Canada were re-analysed using a latent profile analysis to classify individuals along the engagement-burnout continuum. RESULTS: Four clusters were identified: engaged (10.8%), ineffective (18.9%), overextended (29.6%) and burnout (40.7%). These results indicate that most participants (89.2%) had one, or a combination, of high exhaustion, high depersonalisation and low professional efficacy. LIMITATIONS: This cross-sectional study represents data from one point in time and may be subject to response bias. CONCLUSION: We discuss strategies-particularly long-term, organisational-level interventions-to promote engagement and help address workplace issues contributing to inefficacy, overextension and burnout in the veterinary profession. We also recommend MBI data be analysed via latent profiles to provide a more nuanced view of burnout, allow for earlier recognition of workplace issues and facilitate more meaningful interventions and comparisons across populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".