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Record W1968945796 · doi:10.1525/jer.2012.7.1.1

The Participation of Community Members on Medical Institutional Review Boards

2012· article· en· W1968945796 on OpenAlexaff
Charles W. Lidz, Lorna J. Simon, Antonia V. Seligowski, Suzanne Myers, William Gardner, Philip J. Candilis, Robert M. Arnold, Paul S. Appelbaum

Bibliographic record

VenueJournal of Empirical Research on Human Research Ethics · 2012
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsColumbia College
FundersNational Center for Advancing Translational SciencesNational Center for Research ResourcesNational Cancer Institute
KeywordsConfidentialityInstitutional review boardInformed consentFocus groupMedical educationPsychologyProtocol (science)Family medicinePublic relationsMedicinePolitical scienceAlternative medicineSociologyLaw

Abstract

fetched live from OpenAlex

The goal of this study was to describe the contributions of community members (unaffiliated members) who serve on institutional review boards (IRBs) at large medical research centers and to compare their contributions to those of other IRB members. We observed and audiotaped 17 panel meetings attended by community members and interviewed 15 community members, as well as 152 other members and staff. The authors coded transcripts of the panel meetings and reviewed the interviews of the community members. Community members played a lesser role as designated reviewers than other members. They were infrequently primary reviewers and expressed hesitation about the role. As secondary or tertiary reviewers, they were less active participants than other members in those roles. Community members were more likely to focus on issues related to confidentiality when reviewing an application than other reviewers. When they were not designated reviewers, however, they played a markedly greater role and their discussion focused more on consent disclosures than other reviewers. They did not appear to represent the community so much as to provide a nonscientific view of the protocol and the consent form.

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)0.443
metaresearch head score (Gemma)0.739
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Science and technology studies, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.4430.739
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0030.009
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.081
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 head0.966
GPT teacher head0.813
Teacher spread0.154 · 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 designTheoretical or conceptual
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

Citations29
Published2012
Admission routes1
Has abstractyes

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