A Realist Synthesis of Community-Based Interventions in Vector-Borne Diseases
Bibliographic record
Abstract
Randomized control trials have provided evidence that some community-based interventions (CBIs) work in vector-borne diseases (VBDs). Conversely, there is limited evidence on how well those CBIs succeed in producing specific outcomes in different contexts. To conduct a realist synthesis for knowledge translation on this topic, we examined the extent to which realist concepts (context, mechanisms, and outcomes) and their relationships are present in the existing literature on CBIs for VBDs. Articles on CBIs were identified from prior scoping reviews of health interventions for VBDs. Content of the articles was extracted verbatim if it referred either to realist concepts or CBI features. The number of articles and the average number of words extracted per category per CBI were quantified. Content of the articles was scrutinized to inductively gather qualitative evidence on the interactions between realist concepts. We reviewed 41 articles on 17 CBIs from 12 countries. The average number of words used for mechanisms was much lower than those used for outcomes and context (309,474, and 836, respectively). The average number of words used for mechanisms increased when a CBI was described in three or more articles. There were more extensive accounts on CBI features than on mechanisms. It was difficult to gather evidence on the interactions among realist concepts from the content of the articles. Scarce reporting on mechanisms in published articles limits conducting a realist synthesis of CBIs in VBDs. More transdisciplinary research that goes beyond the biomedical paradigm is needed to boost the development of intervention mechanisms in this field.
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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 | 0.219 | 0.452 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.045 | 0.025 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".