How stakeholder participation can contribute to systematic reviews of complex interventions
Bibliographic record
Abstract
Although patient and public involvement in research is a requirement for research funding in many countries, the knowledge base for how to effectively involve people-and evidence of the effectiveness of involvement-is weak. This article describes how methods used in participatory health research were used to involve patients, clients, providers and community health workers across all stages of a realist review. Sustained involvement enabled better identification of the components of the complex intervention of community-based peer support. It also challenged assumptions of how peer support is constructed, leading the review team to question whether the process of designing and implementing interventions has more influence on effectiveness than previously recognised in empirical studies. We conclude with a discussion on when sustained involvement should be used, and the challenges of incorporating it into the traditional researcher-led approach to systematic reviews.
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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 | 0.768 | 0.898 |
| Meta-epidemiology (narrow) | 0.009 | 0.009 |
| Meta-epidemiology (broad) | 0.018 | 0.014 |
| Bibliometrics | 0.064 | 0.035 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.031 | 0.034 |
| Open science | 0.012 | 0.051 |
| Research integrity | 0.019 | 0.013 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".