Conceptualising, operationalising, and measuring trust in participatory health research networks: a scoping review
Bibliographic record
Abstract
BACKGROUND: There are many 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 discusses 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 to systematically review and synthesize the literature exploring how trust is conceptualised, operationalised, and measured in CBPR and social networks. METHODS: This review follows the guidance and framework of Peters et al. which is underpinned by the widely used framework of Levac and colleagues. Levac and colleagues provided enhancements to the methodological framework of Arksey and O'Malley. We explored several electronic databases including Scopus, Medline, PubMed, Web of Science, CINAHL, Cochrane Library, Google Scholar, and PsychINFO. A search strategy was identified and agreed upon by the team in conjunction with a research librarian. Two independent reviewers screened articles by title and abstract, then by full-text based on pre-determined exclusion/inclusion criteria. A third reviewer arbitrated discrepancies regarding inclusions/exclusions. A thematic analysis was then conducted to identify relevant themes and sub-themes. RESULTS: Based on the 26 extracted references, several key themes and sub-themes were identified which highlighted the complexity and multidimensionality of trust as a concept. Our analysis revealed an additional emergent category that highlighted another important dimension of trust-outcomes pertaining to trust. Further, variation within how the studies conceptualised, operationalised, and measured trust was illuminated. Finally, the multidimensionality of trust provided important insight into how trust operates as a context, mechanism, and outcome. CONCLUSIONS: Findings provide support for future research to incorporate trust as a lens to explore the social-relational aspects of partnerships and the scope to develop interventions to support trust in partnerships.
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 | 0.150 | 0.385 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.040 | 0.040 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".