Investigation of burnout syndrome and job‐related risk factors in veterinary technicians in specialty teaching hospitals: a multicenter cross‐sectional study
Bibliographic record
Abstract
OBJECTIVES: To investigate veterinary technician burnout and associations with frequency of self-reported medical error, resilience, and depression and job-related risk factors. DESIGN: Cross-sectional observational study using an anonymous survey conducted between November 2017 and June 2018. SETTING: Four referral teaching hospitals in the United States and Canada. SUBJECTS: A total of 344 veterinary technicians were invited to participate. Response rate was 95%. Overall 256 surveys were ultimately analyzed. INTERVENTIONS: Burnout, depression, and resilience were measured using validated instruments. Respondents reported perceptions of workload, working environment, and medical error frequency. Associations between burnout and factors related to physical work environment, workload and schedule, compensation package, interpersonal relationships, intellectual enrichment, and exposure to ethical conflicts were analyzed. MEASUREMENTS AND MAIN RESULTS: Burnout, characterized by high emotional exhaustion, depersonalization, and low sense of personal accomplishment was common, and was positively associated with perceived medical errors, desire to change career, and depression. Burnout levels on all 3 burnout subscales were higher in this population than previously reported for a contemporaneous group of trauma nurses working with human patients (P < 0.05). Burnout was negatively associated with resilience. Respondents' feelings of fear or anxiety around supervisor communications, perception that patient load was too high to allow for excellent patient care, and perceived lack of available assistance during sudden workload increases were all associated with burnout. CONCLUSIONS: Burnout in veterinary technicians is common and is associated with numerous undesirable outcomes. Work-related interventions to reduce burnout should focus on improving supervisor relationships and maintaining an appropriate patient:caregiver ratio.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".