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Record W4323979922 · doi:10.18192/aporia.v15i1.6487

“Very Similar to Having a Pimp”: Community Advisory Board Members’ Experiences in Health-Related Community-Based Participatory Research

2023· article· en· W4323979922 on OpenAlexaffvenueabout
Laurel Schmanda, Audrey R. Giles, Carrie Martin, Michael J. Liddell

Bibliographic record

VenueAporia · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsCAAN Communities, Alliances & NetworkUniversity of Ottawa
Fundersnot available
KeywordsCommunity-based participatory researchParticipatory action researchGeneral partnershipCompensation (psychology)Focus groupPublic relationsCitizen journalismSociologyCommunity engagementMedical educationPsychologyPolitical scienceMedicineSocial psychology

Abstract

fetched live from OpenAlex

Despite the abundance of research on the experiences of researchers with community-based participatory research (CBPR), there has been comparatively little published on CAB members’ experiences. The goal of this research was to analyze the lived experiences of a small sample of CAB members in recent health-focused CBPR in Canada, with a particular focus on areas for improvement. We found that CAB members in CBPR experienced low perceived value from researchers due to communication patterns, education differences, and inadequate compensation. These issues may be mitigated through increased CAB member engagement throughout the research process, adequate compensation, and improved emotional support. This study demonstrates that if CBPR methodologies are to live up to their promise, it is crucial that CAB members are enabled to work in true partnership with researchers, receive adequate compensation that is meaningful to them, and are supported throughout the process.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.063
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0630.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0070.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0010.002

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 head0.867
GPT teacher head0.697
Teacher spread0.170 · 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

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreEmpirical

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

Citations2
Published2023
Admission routes3
Has abstractyes

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