Intersectoral action for health equity: a rapid systematic review
Bibliographic record
Abstract
BACKGROUND: Action on the social determinants of health is considered a necessary approach to improving health equity. Most of the social determinants of health lie outside the sphere of the health sector and thus collaboration with governmental and non-governmental sectors outside of health are required to develop policies and programs to improve health equity. Case studies of intersectoral action are available, however there is limited information about the impact of intersectoral action on the social determinants of health and health equity. METHODS: Search and retrieval of literature published between 2001 and 2011 was conducted in 6 databases. A staged screening of titles and abstracts, and later full-text, was conducted by two independent reviewers. Reviewers independently assessed the quality of the articles deemed relevant for inclusion. Data were extracted and synthesized in narrative format for all included studies, conducted by one reviewer and checked by another. RESULTS: 17 articles of varied methodological quality met the inclusion criteria. One systematic review investigating partnership interventions found mixed and limited impacts on health outcomes. Primary studies evaluating the impact of upstream and midstream interventions showed mixed effects. Downstream interventions were generally moderately effective in increasing the availability and use of services by marginalized communities. CONCLUSIONS: The literature evaluating the impact of intersectoral action on health equity is limited. The included studies identified reveal a moderate to no effect on the social determinants of health. The evidence on the impact of intersectoral action on health equity is even more limited. The lack of evidence should not be interpreted as a lack of effect. Rigorous evaluations of intersectoral action are needed to strengthen the evidence base of this public health practice.
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.
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 | 0.129 | 0.281 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.015 |
| Bibliometrics | 0.047 | 0.031 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".