The double whammy: Advanced medical training and parenting
Bibliographic record
Abstract
Clinicians may become parents during their clinical training and may be exposed to several challenges in career development, burnout and work-life balance. Previous research findings have reported that stressors facing trainees with children warrant greater attention from graduate medical institutions. Additionally, parenting-related information and considerations about the needs of trainees with children across clinical specialties are needed to inform institutional and national policies. A quantitative approach was used to examine clinical trainees' perceptions and experiences of parenting in relation to different specialties, sociodemographic traits, levels of support, and other potential factors influencing their residency and fellowship training and well-being. We used a survey that was distributed to all University of Toronto medical trainees (2214) via email correspondence and social media platforms. The trainees were asked to base their answers on their experience during the academic year of 2019 to 2020 (before the Coronavirus Disease 2019-related shutdown). Our study revealed that clearly, burnout is a concern for physicians who are raising children while in training. Notably, it was higher among younger aged trainees and those beginning their training journey including, first-year fellows and second-year residents, in addition to parents with toddlers. Moreover, female residents and male fellows showed higher burnout than their counterparts. Institutional support was associated with lower rates of burnout, evidenced by access to opportunities, allowing time to breastfeed/express milk and having access to medical care. We found multiple independent and significant factors affecting their rate of burnout including limited access to opportunities, lack of a self-care routine and absence of social community outside of work. The results show the importance of creating a positive learning experience for trainees juggling parenting and training needs, especially those starting their training both as residents and as fellows and those with younger children. Interventions can be categorized into those targeted at the individual and family levels, and institutional levels, with the overarching goal of balancing training and parenting. This can be achieved by fostering learning environments that prevent and decrease burnout and enhance the well-being of trainees and their families, which can start with ensuring trainees are aware of available resources and possible accommodations.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".