A Cross Sectional Survey of Recruitment Practices, Supports, and Perceived Roles for Unaffiliated and Non-scientist Members of IRBs
Bibliographic record
Abstract
BACKGROUND: Institutional Review Boards (IRBs) are federally mandated to include both nonscientific and unaffiliated representatives in their membership. Despite this, there is no guidance or policy on the selection of unaffiliated or non-scientist members and reports indicate a lack of clarity regarding members' roles. In the present study we sought to explore processes of recruitment, training, and the perceived roles for unaffiliated and non-scientist members of IRBs. METHODS: We distributed a self-administered REDCap survey of members of the Association for the Accreditation of Human Research Protection Programs familiar with IRB member recruitment. The survey included closed and open-ended questions regarding: the operation of the HRPP/IRB(s), how unaffiliated and non-scientist members are recruited, whether they had faced challenges recruiting for these roles, and training and mentorship offered. The survey also collected information regarding the perceived value and roles of unaffiliated and non-scientist members. RESULTS: 76 responses were included in the analysis (38% completion rate). The most common approach for recruitment was referral from current IRB members, with almost half of respondents indicating challenges recruiting unaffiliated members. Over 75% indicated no additional training was provided to unaffiliated or non-scientist members compared to affiliated or scientist members. Most common supports provided were travel/parking expenses and honoraria. Commonly perceived roles were to provide an independent voice from the participant perspective, notably regarding consent processes and materials. CONCLUSIONS: Respondents indicated challenges in defining unaffiliated and non-scientist members and limited practices toward recruitment and support. Future work should more closely examine the challenges in defining these roles and applying the definitions in practice, as well as strategies that may improve recruitment and retention of unaffiliated and non-scientist members.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".