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Record W4211043714 · doi:10.21203/rs.3.rs-123462/v1

Conceptualising, Operationalising, and Measuring Trust in Participatory Health Research Networks: a Scoping Review

2020· review· en· W4211043714 on OpenAlexfundno aff
Meghan Gilfoyle, Anne MacFarlane, Jon Salsberg

Bibliographic record

VenueResearch Square · 2020
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchUniversity of Limerick
KeywordsCitizen journalismEnvironmental planningSociologyParticipatory action researchEnvironmental resource managementBusinessPolitical scienceGeographyEnvironmental science

Abstract

fetched live from OpenAlex

Abstract BackgroundThere 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 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 to systematically review and synthesize the literature exploring how trust is conceptualised, operationalised, and measured in CBPR and social networks.MethodsThis review follows guidelines from Levac et al, which follow 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.ConclusionsFindings 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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models agreeAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.130
metaresearch head score (Gemma)0.375
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.130
Threshold uncertainty score0.685

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1300.375
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0380.037
Science and technology studies0.0040.007
Scholarly communication0.0150.017
Open science0.0040.009
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.964
GPT teacher head0.811
Teacher spread0.152 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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