The Complex Relationship Between Veterinarian Mental Health and Client Satisfaction
Bibliographic record
Abstract
A relatively high risk of poor mental health has been described among Canadian veterinarians, but no published studies have explored the impact that veterinarian mental health may have on veterinary clients and patients. In order to investigate the association between veterinarian mental health and veterinary client satisfaction, veterinarians were randomly sampled and recruited throughout southwestern Ontario, Canada, from November, 2017, through January, 2019. Sixty participating veterinarians completed an enrollment survey that included psychometric scales measuring resilience, perceived stress, anxiety, depression, emotional distress, emotional exhaustion, depersonalization, personal accomplishment, burnout, secondary traumatic stress, and compassion satisfaction. Nine hundred and ninety-five companion animal clients of these veterinarians were recruited in-clinic over 2-3 days and completed a post-appointment survey including the Client Satisfaction Questionnaire. The associations between clients' satisfaction scores (as the outcome variable) and each of the veterinarians' mental health measures (as the explanatory variables) were assessed using separate, multilevel, multivariable linear regression models. The associations between client satisfaction and veterinarian mental health measures were non-linear and complex; in several of the models, relatively higher client satisfaction was unexpectedly associated with poor veterinarian mental health states, while lower client satisfaction was associated with mental health scores suggesting wellness. Given that client satisfaction may impact client adherence to medical recommendations, client loyalty, and business income, the association with veterinarian mental health may have broad implications and warrants further investigation.
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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 | 0.001 | 0.007 |
| 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 | 0.000 |
| 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 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".