Experience of pregnancy during family medicine residency
Bibliographic record
Abstract
OBJECTIVE: To explore the challenges that childbearing family medicine residents encounter during postgraduate training and to understand the available support systems. DESIGN: Descriptive qualitative research study. SETTING: British Columbia, Canada. PARTICIPANTS: Nine University of British Columbia family medicine residents who experienced pregnancy during their residencies between 2014 and 2018. METHODS: Semistructured telephone interviews with family medicine residents were conducted until data reached saturation. Audiorecorded interviews were transcribed and analyzed using content analysis with an iterative approach to elucidate themes. Member checking and peer debriefing were used to ensure the rigour of the findings. MAIN FINDINGS: The participants reported various unique challenges during pregnancy, maternity leave, and return to work. Residents during pregnancy tended to prioritize work over one's own well-being and reported an increased level of perceived adverse symptoms. During maternity leave, residents reported postpartum depression, anxiety, and conflict between the roles of parent and physician. Upon return to work, participants perceived a decrease in their clinical function and reported feelings of guilt and anxiety because of the shared burden of residency with family. Residents found their programs supportive throughout pregnancy and maternity leave; however, a decrease in support upon return to work was a recurring theme in responses. CONCLUSION: Pregnancy during family medicine residency has unique challenges, necessitating support from programs, preceptors, and colleagues. Further resources and incentives are needed to facilitate the transition back to work after maternity leave.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".