Offering Results to Research Subjects: U.S. Institutional Review Board Policy
Bibliographic record
Abstract
BACKGROUND: This study aims to determine the nature of United States Institutional Review Board (IRB) policy in a broad spectrum of research settings regarding the return of results to study participants. METHOD: IRB policies or standard operating procedures of 207 Medical School, Industry and Non-medical School IRBs were examined on-line to determine if they incorporated specific reference to the return of results to participants at the conclusion of the research. RESULTS: The majority of IRBs had no available policy regarding the return of research results to participants [56% (n = 116)]. A further third 136.3% (n = 75)] had policies that were defined as vague or that only indirectly mentioned the return of results. Medical School IRBs were more likely to have a policy than Industry or Non-medical University IRBs, respectively (odds ratio, 4.63; 95% confidence interval, 1.84 to 11.66 and odds ratio, 3.03; 95 % confidence interval, 1.75 to 5.25). Few provided any guidance as to the process of return of results. Of the IRBs that had a research results policy, 54.9% (n = 50) specifically addressed genetic research. CONCLUSIONS: Our findings demonstrate a marked lack of uniformity in IRB policy regarding the return of study results with over half providing no guidance.
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 | 0.471 | 0.361 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".