Gender disparity in the impact of COVID‐19 on childcare responsibilities and professional standing among specialty small animal surgeons
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to report the effects of the COVID-19 pandemic on childcare responsibilities, mental health, and professional relationships of small animal surgeons. STUDY DESIGN: Voluntary, non-incentivized, anonymized 40-question internet survey deployed November 2021-February 2022. SAMPLE POPULATION: A total of 333 completed surveys from veterinary surgeons and residents in the USA. METHODS: Respondents provided information regarding demographics, family composition, effects of the COVID-19 pandemic on childcare, impact of work-life balance changes on mental health, and interpersonal work relationships. The influence of variables such as age and gender on these data was analyzed. Associations between demographics and responses were analyzed (p < .05). RESULTS: Families were most commonly categorized as "children and a partner" (139/312, 44.6%), followed by "partner and no children" (100/312, 32%). A total of 46.5% (67/145) of respondents reported disruptions in school schedules affected their work schedule. Female respondents were most likely affected (OR = 2.2, p = .047). Respondents experiencing stress due to disruptions in work-life balance reported three or more feelings of mental distress and were more likely to be female (p < .001). Female gender was associated with a delay in promotion, adverse effects on relationships with colleagues, and negative effects on relationships with administration (p = .016, p < .001, p = .01). CONCLUSION: The COVID-19 pandemic affected childcare responsibilities, professional standing, and the mental health of veterinary surgeons. Female gender was the most common variable associated with dysregulation of work-life balance. CLINICAL IMPACT: Identifying variables assists in creating strategies that improve job satisfaction and serve as a foundation for enhancing the profession's preparedness for future disruptions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".