Realist Approach to Social Policies (RASP) study to reduce socioeconomic health inequalities through systems change: protocol for a research project combining mixed-methods realist research with institutional action research
Bibliographic record
Abstract
INTRODUCTION: Health inequalities are rooted in inequality in vital resources for health, including financial resources, a supportive informal network, a stable living situation, work or daytime activities or education and literacy. About 25% of Dutch citizens experience deprivation of such resources. Social policy consists of crucial instruments for improving resources in those groups but can also have adverse effects and lead to additional burdens. This project aims to contribute to the reduction of health inequalities through (1) a better understanding of how social policy interventions can contribute to reducing health inequality through the redistribution of burdens and resources and (2) developing anticipatory governance strategies to implement those insights, contributing to a change in social policy systems. METHODS AND ANALYSIS: Two systems approaches are combined for establishing a systems change in the Netherlands. First, a realist approach enables insights into what in social policy interventions may impact health outcomes, for whom and under what circumstances. Second, an institutional approach enables scaling up these insights, by acknowledging the crucial role of institutional actors for accomplishing a systems change. Together with stakeholders, we perform a realist review of the literature and identify existing promising social policy interventions. Next, we execute mixed-methods realist evaluations of selected social policy interventions in seven municipalities, ranging from small, mid-size to large, and in both urban and rural settings. Simultaneously, through action research with (national) institutional actors, we facilitate development of anticipatory governance strategies. ETHICS AND DISSEMINATION: This study is not liable to the Medical Research Involving Subjects Act (WMO). Informed consent to participate in the study is obtained from participants for the use of all forms of personally identifiable data. Dissemination will be codeveloped with target populations and includes communication materials for citizens, education materials for students, workshops, infographics and decision tools for policy-makers and publications for professionals.
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 | 0.084 | 0.072 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.083 | 0.018 |
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".