Influences on intentions for obstetric practice among family physicians and residents in Canada: an explorative qualitative inquiry
Bibliographic record
Abstract
Abstract Background Many family medicine residency graduates indicate a desire to provide obstetric care, but a low proportion of family physicians provide obstetric care within their practice. This suggests personal preference alone may not account for the low proportion of FPs who ultimately provide full obstetric care. If decisionmakers plan to augment number of family physicians providing obstetric care, barriers to the provision of such care must first be identified. Within this paper, we explore the perspectives of both family practice residents and early-career family physicians on the factors that shaped their decision to provide obstetric care. Methods In this qualitative study, we analyzed a subset of interview data from British Columbia, Ontario, and Nova Scotia (n = 18 family practice residents; n = 39 early-career family physicians). We used thematic analysis to analyze data relevant to obstetric care practice, applying the socio-ecological model and comparing themes across participant types, gender, and province. Results Participants described influences affecting their decision about providing obstetric care. Key influencing factors aligned with the levels of the socio-ecological model of public policy (e.g., liability), community (e.g., community needs), organizational (e.g., obstetric care trade-offs, working in teams, sufficient exposure in training), interpersonal practice preferences (e.g., impact on family life, negative interactions with other healthcare professionals), and individual factors (e.g., defining comprehensive care as “everything but obstetrics”). Many participants were interested in providing obstetric care within their practice but did not provide such care. Participants’ decision-making around providing or not providing obstetric care included considerations of personal preferences and outside influences. Conclusions Individual-level factors alone do not account for the decrease in the type and amount of obstetric care offered by family physicians. Instead, family physicians’ choice to provide or not provide obstetric care is influenced by factors at higher levels of the socio-ecological model. Policymakers who want to encourage obstetrics practice by family physicians should implement interventions at the public policy, community, organizational, interpersonal, and individual levels.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".