Analysis of Research Ethics Board Approval Times in an Academic Department of Medicine
Bibliographic record
Abstract
As part of an ongoing effort to better understand barriers to academic research, we reviewed and analyzed the process of research ethics applications, focusing on ethics approval time, within the Department of Medicine from 2006 to 2011. A total of 1,268 applications for approval to use human subjects in research were included in our analysis. Three variables, risk category (minimal vs. non-minimal risk), type of funding, and year of submission, were statistically significant for prediction of ethics approval time, with risk status being the most important of these. The covariate-adjusted mean time for approval for minimal risk studies (35.7 days) was less than half that of non-minimal risk protocols (76.5 days). Studies funded through a for-profit sponsor had significantly longer approval times than those funded through other means but were also predominantly (87%) non-minimal risk protocols. Further investigations of the reasons underlying the observed differences are needed to determine whether improved training for research ethics board (REB) members and/or greater dialogue with investigators may reduce the lengthy approval times associated with non-minimal risk protocols.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | MetaresearchResearch integrity Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.564 | 0.644 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.020 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.139 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".