Equity considerations when making decisions to scale-up health interventions: a review and analysis of scalability assessment resources
Bibliographic record
Abstract
BACKGROUND: Scale-up of effective interventions is needed to achieve population health outcomes. Before scaling-up, many assess the potential scalability of interventions using a scalability assessment resource. Given the critical importance of reducing health inequities, understanding how equity is considered in these resources merits investigation. We sought to determine if scalability resources consider equity and, if so, how equity is applied. METHODS: A structured search of the literature identified 28 scalability resources. A deductive content analysis was undertaken by two independent coders to identify key aspects of equity concepts within the resources. RESULTS: Of the resources identified, only half explicitly mentioned equity or equity-related terms; none provided a clear definition of equity. Of the half that mention equity, only three included an equity-specific step while only two included equity as a guiding principle. Three-quarters highlighted a range of disadvantaged groups for consideration, with race/ethnicity and socio-economic status being most frequently mentioned. CONCLUSION: This highlights an important gap in current scalability assessment resources and provides an opportunity to improve the systematic consideration of equity. Without more concerted effort to consider equity in scalability assessments, it may result in disadvantaged and marginalized groups being exposed to increasing inequity even when interventions are scaled-up.
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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 | 0.179 | 0.481 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.034 | 0.027 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".