Patient-Centered Care for Women: Delphi Consensus on Evidence-Derived Recommendations
Bibliographic record
Abstract
OBJECTIVE: Patient-centered care (PCC) could reduce gender inequities in quality of care. Little is known about how to implement patient-centered care for women (PCCW). We aimed to generate consensus recommendations for achieving PCCW. METHODS: We used a 2-round Delphi technique. Panelists included 21 women of varied age, ethnicity, education, and urban/rural residence; and 21 health professionals with PCC or women's health expertise. Panelists rated recommendations, derived from prior research and organized by a 6-domain PCC framework, on a 7-point Likert scale in an online survey. We used summary statistics to report response frequencies and defined consensus as when ≥85% panelists chose 5 to 7. RESULTS: The response rate was 100%. In round 1, women and professionals retained 46 (97.9%) and 42 (89.4%) of 47 initial recommendations, respectively. The round 2 survey included 6 recommendations for women and 5 recommendations for professionals (did not achieve consensus in round 1 or were newly suggested). In round 2, women retained 2 of 6 recommendations and professionals retained 3 of 5 recommendations. Overall, 49 recommendations were generated. Both groups agreed on 44 (94.0%) recommendations (13 retained by 100% of both women and clinicians): fostering patient-physician relationship (n = 11), exchanging information (n = 10), responding to emotions (n = 4), managing uncertainty (n = 5), making decisions (n = 8), and enabling patient self-management (n = 6). CONCLUSION: The recommendations represent the range of PCC domains, are based on evidence from primary research, and reflect high concordance between women and professional panelists. They can inform the development of policies, guidelines, programs, and performance measures that foster PCCW.
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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 | 0.289 | 0.246 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.005 | 0.007 |
| 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".