Closing the gap on healthcare quality for equity-deserving groups: a scoping review of equity-focused quality improvement interventions in medicine
Bibliographic record
Abstract
INTRODUCTION: Quality improvement (QI) efforts are critical to promoting health equity and mitigating disparities in healthcare outcomes. Equity-focused QI (EF-QI) interventions address the unique needs of equity-deserving groups and the root causes of disparities. This scoping review aims to identify themes from EF-QI interventions that improve the health of equity-deserving groups, to serve as a resource for researchers embarking on QI. METHODS: In adherence with Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines, several healthcare and medical databases were systematically searched from inception to December 2022. Primary studies that report results from EF-QI interventions in healthcare were included. Reviewers conducted screening and data extraction using Covidence. Inductive thematic analysis using NVivo identified key barriers to inform future EF-QI interventions. RESULTS: Of 5,330 titles and abstracts screened, 36 articles were eligible for inclusion. They reported on EF-QI interventions across eight medical disciplines: primary care, obstetrics, psychiatry, paediatrics, oncology, cardiology, neurology and respirology. The most common focus was racialised communities (15/36; 42%). Barriers to EF-QI interventions included those at the provider level (training and supervision, time constraints) and institution level (funding and partnerships, infrastructure). The last theme critical to EF-QI interventions is sustainability. Only six (17%) interventions actively involved patient partners. DISCUSSION: EF-QI interventions can be an effective tool for promoting health equity, but face numerous barriers to success. It is unclear whether the demonstrated barriers are intrinsic to the equity focus of the projects or can be generalised to all QI work. Researchers embarking on EF-QI work should engage patients, in addition to hospital and clinic leadership in the design process to secure funding and institutional support, improving sustainability. To the best of our knowledge, no review has synthesised the results of EF-QI interventions in healthcare. Further studies of EF-QI champions are required to better understand the barriers and how to overcome them.
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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 | 0.074 | 0.218 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".