Health mediation intervention at the base of a social housing complex in Seine-Saint-Denis, France: a mixed-methods, realistic evaluation protocol
Bibliographic record
Abstract
Health mediation, similar to health navigation in the United States or Canada, is known by various terms worldwide. In France, health mediation has historically been implemented by civil society organizations to support hard-to-reach populations. Health mediation is increasingly considered by health authorities as a valuable tool for health promotion to reduce health inequalities. However, systematic evaluations of its effects are scarce, making it difficult for decision-makers to generalize health mediation as a health policy. Our study aims to bridge this gap, by framing a research protocol to evaluate a health mediation intervention. The intervention consists of setting up a biweekly mobile booth using an 'outreach' approach at the base of a social housing complex in 12 neighborhoods of Seine-Saint-Denis with marked indicators of social deprivation. We chose a realistic evaluation approach and a mixed-methods methodology, which is the best fit for assessing complex interventions such as the one we aim to assess. Realistic evaluation is a relatively new approach, and sharing studies based on this type of epistemological and methodological approach is required. This study aims to contribute to the reflection on and the production of standard tools to ensure that the use of this evaluation approach is improved.
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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 | 0.053 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".