Research Ethics Committees as an intervention point to promote a priori sample size calculations
Bibliographic record
Abstract
Studies in the health and life sciences often use sample sizes too small to robustly answer the research questions at hand. By performing a formal sample size calculation before beginning a study, researchers can overcome this issue. One potential avenue to promote the uptake of sample size calculation is through Research Ethics Committees (RECs). We assessed template ethics submission forms from 10 RECs from research-intensive universities in each of the United States and the United Kingdom (for 20 forms in total). We found that 19 of them requested that researchers provide the planned sample size, 12 requested a justification of the sample size, and 6 requested a sample size calculation. Future research could investigate the effectiveness of sample size requests from RECs and compare this approach to intervening through other mechanisms, such as Scientific Review Committees or funding agencies.
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 | 0.658 | 0.778 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".