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Record W2555776032 · doi:10.1177/1556264616675202

“. . . I’ve Gone Through This My Own Self, So I Practice What I Preach . . . ”

2016· article· en· W2555776032 on OpenAlexafffund
Catherine Slack, Siya Thabethe, Graham Lindegger, Limbanazo Matandika, Peter A. Newman, Philippa Kerr, Douglas Wassenaar, Surita Roux, Linda‐Gail Bekker

Bibliographic record

VenueJournal of Empirical Research on Human Research Ethics · 2016
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchNational Institutes of HealthNational Institute for Health and Care Research
KeywordsVoluntarinessThematic analysisInformed consentFocus groupVaccine trialPsychologyCommunity engagementHuman immunodeficiency virus (HIV)Public relationsMedical educationQualitative researchMedicineAlternative medicineSociologyPolitical scienceFamily medicine

Abstract

fetched live from OpenAlex

There has not been enough study of the processes by which site staff help participating community members and potential participants to understand complicated concepts for HIV vaccine trials. This article describes strategies reported in six focus group discussions with Community Advisory Board members, educators, and consent counselors at an active HIV vaccine trial site in South Africa. Thematic analysis identified a considerable range of strategies, and findings suggest that such staff do not only try to promote understanding of critical information but also try to build trust in communicated information, to respect cultural differences, and to promote voluntariness. Findings also suggest occasional tensions between these implicit goals. Actual engagement and consent encounters at HIV vaccine trial sites should be observed, recorded, and analyzed; and the relationship between practices and valued outcomes should be assessed. These efforts may help to make consent-related encounters as "potent" as possible given finite resources.

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 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)0.018
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.998
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.035
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.017
Scholarly communication0.0040.006
Open science0.0010.005
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0080.004

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 head0.916
GPT teacher head0.770
Teacher spread0.146 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2016
Admission routes2
Has abstractyes

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