What women expect of family physicians as maternity care providers.
Bibliographic record
Abstract
OBJECTIVE: To explore women's expectations and experiences of family physicians as maternity care providers. DESIGN: In-depth semistructured one-on-one interviews were conducted at 36 weeks' gestation and at 6 weeks postpartum. SETTING: Family practices in Halifax, NS. PARTICIPANTS: Five female family physicians recruited a total of 6 low-risk primigravida women. Five of the 6 women completed follow-up interviews. METHOD: Interviews lasting 1 to 2 hours were conducted using an unstructured interview guide. Interviews were audiotaped and transcribed verbatim. Data were analyzed using a grounded-theory approach. MAIN FINDINGS: Women's expectations fell into 4 main categories: informational support, emotional support, advocacy, and competent professional care. Women's expectations of physicians were consistent in some areas and varied in others. All women expected their physicians to communicate information about their medical care, listen to and respect their wishes, and provide them with competent medical care. Some women expected their physicians to provide emotional support and help with decision making, while others did not. Uncertainty about the role of family physicians in helping women prepare for birth was also evident. Women in our study described a range of roles for family physicians as maternity care providers. These roles reflected a holistic style of care that addresses the psychosocial as well as the biomedical needs of women giving birth. CONCLUSION: Research is needed to define family physicians' roles as maternity care providers and to describe how these roles fit with similar roles filled by other maternity care providers.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".