The Participation of Community Members on Medical Institutional Review Boards
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.095 | 0.207 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".