A tool to assess alignment between knowledge and action for health equity
Bibliographic record
Abstract
Advancing health equity is a central goal and ethical imperative in public and global health. Though the commitment to health equity in these fields and among the health professions is clear, alignment between good equity intentions and action remains a challenge. This work regularly encounters the same power structures that are known to cause health inequities. Despite consensus about causes, health inequities persist-illustrating an uncomfortable paradox: good intentions and good evidence do not necessarily lead to meaningful action. This article describes a theoretically informed, reflective tool for assessing alignment between knowledge and action for health equity. It is grounded in an assumption that progressively more productive action toward health inequities is justified and desired and an explicit acceptance of the evidence about the socioeconomic, political, and power-related root causes of health inequities. Intentionally simple, the tool presents six possible actions that describe ways in which health equity work could respond to causes of health inequities: discredit, distract, disregard, acknowledge, illuminate, or disrupt. The tool can be used to assess or inform any kind of health equity work, in different settings and at different levels of intervention. It is a practical resource against which practice, policy, or research can be held to account, encouraging steps toward equity- and evidence-informed action. It is meant to complement other tools and training resources to build capacity for allyship, de- colonization, and cultural safety in the field of health equity, ultimately contributing to growing awareness of how to advance meaningful health equity action.
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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 | 0.084 | 0.289 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".