How is patient-centred care conceptualized in women’s health: a scoping review
Bibliographic record
Abstract
BACKGROUND: Gendered disparities in health care delivery and outcomes are an international problem. Patient-centred care (PCC) improves patient and health system outcomes, and is widely advocated to reduce inequities. The purpose of this study was to review published research for frameworks of patient-centred care for women (PCCW) that could serve as the basis for quality improvement. METHODS: A scoping review was conducted by searching MEDLINE, EMBASE, CINAHL, SCOPUS, Cochrane Library, and Joanna Briggs index for English-language quantitative or qualitative studies published from 2008 to 2018 that included at least 50% women aged 18 years or greater and employed or generated a PCCW framework. Findings were analyzed using a 6-domain PCC framework, and reported using summary statistics and narrative descriptions. RESULTS: A total of 9267 studies were identified, 6670 were unique, 6610 titles were excluded upon title/abstract screening, and 11 were deemed eligible from among 60 full-text articles reviewed. None were based on or generated a PCCW framework, included solely women, or analyzed or reported findings by gender. All studies explored or described PCC components through qualitative research or surveys. None of the studies addressed all 6 domains of an established PCC framework; however, additional PCC elements emerged in 9 of 11 studies including timely responses, flexible scheduling, and humanized management, meaning tailoring communication and treatment to individual needs and preferences. There were no differences in PCC domains between studies comprised primarily of women and other studies. CONCLUSIONS: Given the paucity of research on PCCW, primary research is needed to generate knowledge about PCCW processes, facilitators, challenges, interventions and impacts, which may give rise to a PCCW framework that could be used to plan, deliver, evaluate and improve 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 | 0.043 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".