Bibliographic record
Abstract
Burnout is on the rise among the helping professions such as human and veterinary medicine, and negatively affects personal and professional wellbeing, and the provision of quality care to clients and animals. Even more significant is that veterinarians are reported to have the highest incidence rate of suicide among all occupations, and twice as high as physicians and dentists (1). Indeed, 85% of American Veterinary Medical Association convention attendees indicated that stress and burnout (includes compassion fatigue) were the most important wellness issues affecting the veterinary community (2). Seventy-six percent believed that there were not adequate resources to deal with wellness issues (2). Much research attention is now being focused on the emotional exhaustion component of burnout, as it has been increasingly shown to be correlated with physical and mental health. Progress is also being made to relate other variables such as work/life conflict, and communications with burnout. A case in point was our research study among Canadian physicians. We found that having positive emotions and responsive communications with patients reduced burnout, but when difficult emotions were kept hidden or insincere, burnout increased (3). Of the 3 burnout dimensions, emotional exhaustion contributed to symptoms of strain. We also found that less experienced physicians reported higher stress levels than those who were more experienced, and female physicians reported higher stress than their male colleagues. Comparable findings are drawn for veterinary medicine in which Australian female veterinary surgeons and those with less experience also reported higher stress levels (4). Many clients regard their companion animals as cherished family members; consequently, they have high expectations that emotional and medical needs will be met (5). Recognition of the human-companion animal bond is the very core of the bond-centered approach for veterinary practice. It involves recognizing and responding to the unique emotional interchange, in a way that benefits all of the participants involved (6). Thus, good communication is likely to result in strong client relationships, and an indicator of those more likely to follow treatment recommendations (5–6). To be engaging and responsive to clients often involves intense and constant emotions, along with other forms of verbal and non-verbal communications. This is no easy task, as the skills needed to manage people and emotions, emotionally volatile clients, and the feelings that arise from euthanization require practice, time, and patience (4,7). This deep form of caring has a potential to be a risk factor for compassion fatigue and burnout if mental, spiritual, and emotional balance is not maintained (1). Compassion fatigue then is the emotional burden that occurs as the result of continued and excessive exposure to traumatic events that patients and families experience (8).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".