Empathetic Yet Resistant: Accommodating Immigrant Women’s Preferences for Female Providers [36C]
Bibliographic record
Abstract
INTRODUCTION: For women migrating from different religio-cultural environments, having a female obstetrical provider may be particularly important. While accommodating these requests can be seen as providing patient-centered care, the feasibility and ethical questions of gender discrimination and educational implications make it a contentious issue. The objective of this study was to gain obstetricians’ understanding of the importance, effect, and challenges to providing care when immigrant women prefer a female obstetrician. METHODS: A focused ethnography was conducted using purposive sampling of 20 obstetrical providers in Edmonton, Alberta, Canada. Data collection comprised of a single semi-structured interview with participants. Interviews were audio-recorded and transcribed verbatim. Data was managed by a qualitative data analysis software, and analyzed using thematic analysis. RESULTS: A total of 13 female and seven male physicians were interviewed. In line with patient-centered care, physicians recognized the validity, and empathized with immigrant women’s preference for female providers. However, they were resistant to accommodating these requests, stemming from concerns about the extent to which host communities should accommodate immigrant cultural requests, based on the ability of the health system to respond, discerning coercion-free patient decision-making, implications for training and quality of care, and fear of perpetuating and exacerbating gender inequalities in medicine. CONCLUSION: Physicians faced a dilemma trying to balance patient preferences with their own autonomy as a physician and a person. Identifying physician’s values and perspective will enhance understanding of the patient-physician relationship, ultimately progressing towards addressing this issue both philosophically and practically.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".