Methods for evaluating intersectoral action partnerships to address the social determinants of health: a scoping review
Bibliographic record
Abstract
Introduction Many of the social and economic factors that shape conditions for population health and health equity (e.g. income, education and employment) lie outside of the health sector. Intersectoral action (ISA) is pivotal to building diverse partnerships that address these social determinants of health. Despite the significant role of ISA, there are few comprehensive reports from the health sector on how such partnerships are evaluated. The purpose of this scoping review is to provide an overview of examples of ISA partnership evaluations, including the identification of evaluation methods, tools and indicators. Methods A literature search of two academic databases, Embase and MEDLINE, identified seven relevant studies published between 2012 and 2022. Results Common evaluation approaches were network analysis, community- or system- level analysis, partnership evaluation and longitudinal process evaluation. Five of the studies assessed the strength and functionality of partnerships, with reach (e.g. distance between partners) used most frequently as an indicator. Conclusion Despite the complexity of evaluating ISA partnerships, such evaluations are crucial for assessing impacts on health outcomes and social determinants of health, goal achievement, accountability and sustainability. Different evaluation models are available to program planners and evaluators involved in ISA initiatives.
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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 | 0.171 | 0.313 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.051 | 0.037 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".