Connecting knowledge with action for health equity: a critical interpretive synthesis of promising practices
Bibliographic record
Abstract
Connecting knowledge with action (KWA) for health equity involves interventions that can redistribute power and resources at local, national, and global levels. Although there is ample and compelling evidence on the nature, distribution, and impact of health inequities, advancing health equity is inhibited by policy arenas shaped by colonial legacies and neoliberal ideology. Effective progress toward health equity requires attention to evidence that can promote the kind of socio-political restructuring needed to address root causes of health inequities. In this critical interpretive synthesis, results of a recent scoping review were broadened to identify evidence-informed promising practices for KWA for health equity. Following screening procedures, 10 literature reviews and 22 research studies were included in the synthesis. Analysis involved repeated readings of these 32 articles to extract descriptive data, assess clarity and quality, and identify promising practices. Four distinct kinds of promising practices for connecting KWA for health equity were identified and included: ways of structuring systems, ways of working together, and ways of doing research and ways of doing knowledge translation. Our synthesis reveals that advancing health equity requires greater awareness, dialogue, and action that aligns with the what is known about the causes of health inequities. By critically reflecting on dominant discourses and assumptions, and mobilizing political will from a more informed and transparent democratic exercise, knowledge to action for health equity can be achieved.
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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 | 0.192 | 0.230 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.034 | 0.025 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".