Critical examination of knowledge to action models and implications for promoting health equity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Knowledge and effective interventions exist to address many current global health inequities. However, there is limited awareness, uptake, and use of knowledge to inform action to improve the health of disadvantaged populations. The gap between knowledge and action to improve health equity is of concern to health researchers and practitioners. This study identifies and critically examines the usefulness of existing knowledge to action models or frameworks for promoting health equity. METHODS: We conducted a scoping review of existing literature to identify knowledge to action (KTA) models or frameworks and critiqued the models using a health equity support rubric. RESULTS: We identified forty-eight knowledge to action models or frameworks. Six models scored between eight and ten of a maximum 12 points on the health equity support rubric. These high scoring models or frameworks all mentioned equity-related concepts. Attention to multisectoral approaches was the factor most often lacking in the low scoring models. The concepts of knowledge brokering, integrative processes, such as those in some indigenous health research, and Ecohealth applied to KTA all emerged as promising areas. CONCLUSIONS: Existing knowledge to action models or frameworks can help guide knowledge translation to support action on the social determinants of health and health equity. There is a need to further test existing models or frameworks. This process should be informed by participatory and integrative research. There is room to develop more robust equity supporting models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it