Equity in action: a scoping review and meta-framework for embedding equity in quality improvement
Bibliographic record
Abstract
BACKGROUND: There are increasing efforts to include equity in all quality improvement (QI) initiatives. A comprehensive framework to embed equity in QI has been lacking, which acts as a barrier to the QI community from taking action to reduce healthcare inequities. OBJECTIVES: The objectives of this scoping review were to: (1) map and summarise available equity frameworks for QI and (2) create a 'meta-framework' for QI leaders and practitioners, with engagement of people with lived experience of health inequities. METHODS: Articles were identified with searches of four databases (MEDLINE, Embase, PsycInfo and CINAHL) and review of reference lists from included articles. Articles that reported how equity can be meaningfully integrated into QI were included. A qualitative inductive thematic analysis and community member engagement and consultation were completed to clarify recommended strategies for embedding equity in QI. RESULTS: The search strategy yielded 2776 unique articles, with 40 meeting the inclusion criteria. A meta-framework for embedding equity in QI was created that has two enablers: broadening theoretic underpinnings and organisational culture, structures and leadership. The meta-framework also has six domains: (1) engage with people with lived experience of health inequities; (2) define the equity problem and aim; (3) diversify and train the QI team; (4) examine broader root causes; (5) intervene to reduce inequities; and (6) measure impacts on equity. The community member consultation identified key facilitators and common pitfalls in involving community members in QI. CONCLUSION: This meta-framework is a comprehensive resource to integrate equity into all aspects of QI practice. Further study of its implementation is recommended. Revisions to QI guidelines and training curricula are also needed to drive and sustain the embedding of equity in QI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.093 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".