Veterinary house officer perceptions of dimensions of well-being during postgraduate training
Bibliographic record
Abstract
OBJECTIVE: To describe veterinary house officers' perceptions of dimensions of well-being during postgraduate training and to identify potential areas for targeted intervention. SAMPLE: 303 house officers. PROCEDURES: A 62-item questionnaire was generated by use of an online platform and sent to house officers at participating institutions in October 2020. Responses were analyzed for trends and associations between selected variables. RESULTS: 239 residents, 45 rotating interns, and 19 specialty interns responded to the survey. The majority of house officers felt that their training program negatively interfered with their exercise habits, diet, and social engagement. House officers reported engaging in exercise significantly less during times of clinical responsibility, averaging 1.6 exercise sessions/wk (SD ± 0.8) on clinical duty and 2.4 exercise sessions/wk (SD ± 0.9) when not on clinical duty (P < 0.001). Ninety-four percent of respondents reported experiencing some degree of anxiety regarding their physical health, and 95% of house officers reported feeling some degree of anxiety regarding their current financial situation. Overall, 47% reported that their work-life balance was unsustainable for > 1 year; there was no association between specialty and sustainability of work-life balance. Most house officers were satisfied with their current training program, level of clinical responsibility, and mentorship. CLINICAL RELEVANCE: Veterinary house officers demonstrated a poor balance between the demands of postgraduate training and maintenance of personal health. Thoughtful interventions are needed to support the well-being of veterinary house officers.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".