Conceptualising, operationalising and measuring trust in participatory health research networks: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: A participatory approach to co-creating new knowledge in health research has gained significant momentum in recent decades. This is founded on the described benefits of community-based participatory research (CBPR), such as increased relevance of research for those who must act on its findings. This has prompted researchers to better understand how CBPR functions to achieve these benefits through building sustainable research partnerships. Several studies have identified 'trust' as a key mechanism to achieve sustainable partnerships, which themselves constitute social networks. Although existing literature discuss trust and CBPR, or trust and social networks, preliminary searches reveal that none link all three concepts of trust, CBPR and social networks. Thus, we present our scoping review protocol to systematically review and synthesise the literature exploring how trust is conceptualised, operationalised and measured in CBPR and social networks. METHODS AND ANALYSIS: (Scoping studies: advancing themethodology. Implement Sci 2010;5:69), which follow the methodological framework of Arksey and O'Malley. This scoping review explores several electronic databases including Scopus, Medline, PubMed, Web of Science, CINAHL, Cochrane Library, Google Scholar and PsychINFO. Grey literature such as theses/dissertations and reports will be included. A search strategy was identified and agreed on by the team in conjunction with a research librarian. Two independent reviewers will screen articles by title and abstract, then by full text based on pre-determined exclusion/inclusion criteria. A third reviewer will arbitrate discrepancies regarding inclusions/exclusions. We plan to incorporate a thematic analysis. ETHICS AND DISSEMINATION: Ethics is not required for this review specifically. It is a component of a larger study that received ethical approval from the University of Limerick research ethics committee (#2018_05_12_EHS). Translation of results to key domains is integrated through active collaboration of stakeholders from community, health services and academic sectors. Findings will be disseminated through academic conferences, and peer review publications targeting public and patient involvement in health research.
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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 | 0.265 | 0.216 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.050 | 0.016 |
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".