Identifying strategies to implement patient-centred care for women: Qualitative interviews with women
Bibliographic record
Abstract
OBJECTIVE: Patient-centred care (PCC) is one approach for mitigating gendered inequities in health care quality. Little is known about how to implement PCC for women (PCCW). This study explored women's views about PCCW implementation. METHODS: Descriptive analysis of semi-structured qualitative telephone interviews with diverse women about PCC using an established 6-domain PCC framework. RESULTS: Participants were 33 women who varied in health care experience, age, education and setting. Themes were consistent across these characteristics. Women said that clinicians often dismissed their healthcare concerns. We transformed desired PCC elements into strategies to implement PCCW, 27 at the point-of-care (i.e. assume a non-judgmental disposition, demonstrate active listening, elicit questions, acknowledge emotions, explore preferences for treatment, and offer self-care information) and 3 at the system level (education for women/girls and clinicians about PCCW, widespread access to women's-only services or women clinicians). CONCLUSION: Many women experienced suboptimal PCC. By sharing their PCC experiences, women identified PCC elements of importance to them, and insight on actionable point-of-care and system-level strategies to implement PCCW. PRACTICE IMPLICATIONS: This study revealed numerous ways that clinicians can foster PCCW, and insight on how healthcare managers and policy-makers can support PCCW implementation.
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 | 0.027 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".