Multi-level strategies to tailor patient-centred care for women: qualitative interviews with clinicians
Bibliographic record
Abstract
BACKGROUND: Patient-centered care (PCC) is one approach for ameliorating persistent gendered disparities in health care quality, yet no prior research has studied how to achieve patient-centred care for women (PCCW). The purpose of this study was to explore how clinicians deliver PCCW, challenges they face, and the strategies they suggest are needed to support PCCW. METHODS: We conducted semi-structured qualitative interviews (25-60 min) with clinicians. Thirty-seven clinicians representing 7 specialties (family physicians, cardiologists, cardiac surgeons, obstetricians/gynecologist, psychiatrists, nurses, social workers) who manage depression (n = 16), cardiovascular disease (n = 11) and contraceptive counseling (n = 10), conditions that affect women across the lifespan. We used constant comparative analysis to inductively analyze transcripts, mapped themes to a 6-domain PCC conceptual framework to interpret findings, and complied with qualitative research reporting standards. RESULTS: Clinicians said that women don't always communicate their health concerns and physicians sometimes disregard women's health concerns, warranting unique PCC approaches.. Clinicians described 39 approaches they used to tailor PCC for women across 6 PCC domains: foster a healing relationship, exchange information, address emotions/concerns, manage uncertainty, make decisions, and enable self-management. Additional conditions that facilitated PCCW were: privacy, access to female clinicians, accommodating children through onsite facilities, and flexible appointment formats and schedules. Clinicians suggested 7 strategies needed to address barriers of PCCW they identified at the: patient-level (online appointments, transport to health services, use of patient partners to plan and/or deliver services), clinician-level (medical training and continuing professional development in PCC and women's health), and system-level (funding models for longer appointment times, multidisciplinary teamwork to address all PCC domains). CONCLUSIONS: Our research revealed numerous strategies that clinicians can use to optimize PCCW, and health care managers and policy-makers can use to support PCCW through programs and policies. Identified strategies addressed all domains of an established PCC conceptual framework. Future research should evaluate the implementation and impact of these strategies on relevant outcomes such as perceived PCC among women and associated clinical outcomes to prepare for broad scale-up.
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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 | 0.040 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".