Evidence use in equity focused health impact assessment: a realist evaluation
Bibliographic record
Abstract
BACKGROUND: Equity-focused health impact assessment (EFHIA) can function as a framework and tool that supports users to collate data, information, and evidence related to health equity in order to identify and mitigate the impact of a current or proposed initiative on health inequities. Despite education efforts in both the clinical and public health settings, practitioners have found implementation and the use of evidence in completing equity focussed assessment tools to be challenging. METHODS: We conducted a realist evaluation of evidence use in EFHIA in three phases: 1) developing propositions informed by a literature scan, existing theoretical frameworks, and stakeholder engagement; 2) data collection at four case study sites using online surveys, semi-structured interviews, document analysis, and observation; and 3) a realist analysis and identification of context-mechanism-outcome patterns and demi-regularities. RESULTS: We identified limited use of academic evidence in EFHIA with two explanatory demi-regularities: 1) participants were unable to "identify with" academic sources, acknowledging that evidence based practice and use of academic literature was valued in their organization, but seen as less likely to provide answers needed for practice and 2) use of academic evidence was not associated with a perceived "positive return on investment" of participant energy and time. However, we found that knowledge brokering at the local site can facilitate evidence familiarity and manageability, increase user confidence in using evidence, and increase the likelihood of evidence use in future work. CONCLUSIONS: The findings of this study provide a realist perspective on evidence use in practice, specifically for EFHIA. These findings can inform ongoing development and refinement of various knowledge translation interventions, particularly for practitioners delivering front-line public health services.
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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 | 0.471 | 0.560 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".