Current Policies and Support Services for Pregnant and Parenting Veterinary Medical Students and House Officers at United States Veterinary Medical Training Institutions
Bibliographic record
Abstract
Wellness and work-life balance are prominent concerns in the veterinary profession and data suggest that personal relationship-building with peers and family assist veterinary trainees and veterinarians with wellness. The demographics of veterinary medical trainees (students, interns, and residents) have shifted to a female-dominated cohort and veterinary training overlaps with peak reproductive age for the majority of trainees. Despite a robust body of literature in the human medical profession surrounding pregnancy, parenting, and family planning (PPFP) among human medical students, interns, and residents, no comparable data exist within the United States veterinary medical community. This study reviewed policies and support services in place to support PPFP at accredited United States veterinary medical training institutions through the use of an online administrator survey and the review of handbooks and relevant written material. Results from this study highlight a lack of consistency across veterinary medical training institutions for policy and support services for PPFP for trainees, especially related to lactation support and parental leave. Our data can help facilitate the development of standards or best practices for policies and support services that support PPFP among veterinary medical trainees, and opens the dialogue to consider the unique needs of our shifted trainee demographics.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".