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Record W2902705364 · doi:10.1177/1049732318812773

A Seat at the Table: Designing an Activity-Based Community Advisory Committee With People Living With HIV Who Use Drugs

2018· article· en· W2902705364 on OpenAlexafffund
Sarah Switzer, Soo Chan Carusone, Adrian Guţă, Carol Strıke

Bibliographic record

VenueQualitative Health Research · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental HealthUniversity of WindsorCasey HouseMcMaster UniversityYork University
FundersCanadian Institutes of Health ResearchOntario HIV Treatment Network
KeywordsAdvisory committeeHuman immunodeficiency virus (HIV)Table (database)PsychologyGerontologyMedicineFamily medicinePolitical scienceComputer science

Abstract

fetched live from OpenAlex

Recently, scholars have begun to critically interrogate the way community participation functions discursively within community-based participatory research (CBPR) and raise questions about its function and limits. Community advisory committees (CACs) are often used within CBPR as one way to involve community members in research from design to dissemination. However, CACs may not always be designed in ways that are accessible for communities experiencing the intersections of complex health issues and marginalization. This article draws on our experience designing and facilitating Research Rec'-a flexible, and activity-based CAC for a project about the acute-care hospital stays of people living with HIV who use drugs. Using Research Rec' as a case study, we reflect on ethical, methodological, and pedagogical considerations for designing and facilitating CACs for this community. We discuss how to critically reflect on the design and facilitation of advisory committees, and community engagement processes in CBPR more broadly.

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)0.043
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0430.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0200.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.655
GPT teacher head0.591
Teacher spread0.064 · 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; a candidate call from one teacher head, not a consensus.

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

Citations20
Published2018
Admission routes2
Has abstractyes

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