Assessment of Burnout, Professional Fulfillment, and Strategies for Improvement in Veterinary Faculty at a Large Academic Department
Bibliographic record
Abstract
Retention and recruitment of clinical faculty is crucial for the success of quality veterinary education. Clinical faculty in busy teaching hospital environments have the potential to experience significant burnout, though few studies have focused on identifying stressors in this group. The objective of this study was to measure burnout and professional fulfillment in clinical faculty using a recently validated instrument, the Stanford Professional Fulfillment Index (PFI). The survey was distributed to faculty in July 2020, a time that coincided with the COVID-19 pandemic. The survey was completed by 80% (52/65) of survey recipients. Scores for Overall Burnout were significantly higher (p = .027) and Professional Fulfillment scores significantly lower (p < .001) for veterinary faculty when compared with a reference group of academic physicians; 61.7% (29/47) of the faculty met the criteria for burnout, and 20.4% (10/49) met the criteria for professional fulfillment. Overall Burnout and Professional Fulfillment scores were not affected by faculty rank or gender, although interpersonal disengagement was greater in faculty who had worked > 6 years at the institution (p = .032). Responses indicated that faculty valued their work and their patients but faced an excessive workload and lacked autonomy to make changes. Faculty proposed improving efficiency, increasing staffing, and distributing work to technical staff. The PFI is a brief, no-cost instrument validated for measuring burnout and fulfillment in health care workers that can be used to assess well-being among veterinary faculty. Involving faculty in suggesting interventions may yield a variety of creative and actionable options.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".