Exploration of trust in participatory health research partnerships across two timepoints: a network approach
Bibliographic record
Abstract
BACKGROUND: The value of a participatory approach to the generation of evidence for health and social services from a moral, methodological and policy level continues to gain recognition globally. Trust is a crucial mechanism in the participatory health research (PHR) process and is strongly influenced by context. However, gaps remain in conceptualising and operationalising trust over time in PHR partnerships. OBJECTIVE: This case study seeks to address these gaps by exploring the evolution of trust multidimensionally across two timepoints. SETTING AND PARTICIPANTS: Participants in a PHR project called the Public and Patient Involvement (PPI) Ignite Network in Ireland (n=57 (T1); n=56 (T2)) were invited to complete a network survey at two timepoints. The PPI Ignite Network had local and national partners. NETWORK MEASURES: Several core social network measures were calculated at both timepoints to characterise the differences between trust dimensions and between local and national partners. RESULTS: Subtle changes were observed across most network measures over the two timepoints. While there was a slight decrease in the number of connections for each trust dimension throughout the PPI Ignite Network, connections that were consistently nominated in both timepoints increased slightly. Some trust dimensions, such as vulnerability and integrity, were more similar, while others, like integrity and shared values, visions and goals, differed greatly, where national partners consistently received more incoming connections compared with local partners. CONCLUSION: These findings (1) provide empirical support for using social network analysis to operationalise trust comprehensively and multidimensionally over time in a participatory partnership, (2) offer nuanced insights into the trust development process within the PPI Ignite Network and (3) enhance our understanding of trust in the community-based participatory research model.
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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 | 0.077 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".