Understanding the Values, Qualities, and Preferences of Patients in Their Relationships With Obstetrics and Gynecology Providers: Cross-Sectional Survey With a Mixed Methods Approach
Bibliographic record
Abstract
BACKGROUND: The patient-provider relationship in obstetrics and gynecology (OBGYN) is uniquely complex due to the sensitive nature of examinations and topics. Patients often prefer health care providers who share similar racial, ethnic, gender, or linguistic backgrounds, particularly in sensitive health care situations, to improve communication and comfort, though historically, specific gender preferences for OBGYNs have not been evident. OBJECTIVE: This study aims to describe the values, qualities, and preferences of patients in their relationships with OBGYN providers. METHODS: This cross-sectional survey, conducted from October 2019 to December 2019, involved 1039 US OBGYN patients and used a mixed methods approach, integrating quantitative responses and qualitative insights from open-ended questions. Recruitment was facilitated through targeted social media campaigns, and the survey aimed to capture detailed patient preferences and barriers to care by assessing responses on provider traits, patient experiences, and demographic factors. The study's rigorous data collection and analysis were designed to fill gaps identified in previous research on patient-provider relationships in OBGYN care. RESULTS: The findings underscore the paramount importance of trust and comfort, with listening skills identified as crucial. A notable finding is the marked preference for same-gender providers, observed in 80.7% (545/675) of participants. Primary barriers to seeking care reported included daily commitments, highlighting the need for accessible and flexible care options. CONCLUSIONS: The study highlights a significant shift from previous scientific findings in patient preferences toward gender concordance and trust in OBGYN settings, diverging from previous research. These results emphasize the need for patient-centered care and tailored communication strategies to enhance patient experiences and outcomes. Future research should focus on diverse populations to broaden the findings' applicability and explore the impact of recent shifts in health care policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".