THE REPORTING OF IRB REVIEW IN JOURNAL ARTICLES PRESENTING HIV RESEARCH CONDUCTED IN THE DEVELOPING WORLD
Bibliographic record
Abstract
OBJECTIVES: We investigated how often journal articles reporting on human HIV research in four developing world countries mention any institutional review boards (IRBs) or research ethics committees (RECs), and what factors are involved. METHODS: We examined all such articles published in 2007 from India, Nigeria, Thailand and Uganda, and coded these for several ethical and other characteristics. RESULTS: Of 221 articles meeting inclusion criteria, 32.1% did not mention IRB approval. Mention of IRB approval was associated with: biomedical (versus psychosocial) research (P=0.001), more sponsor-country authors (P=0.003), sponsor-country corresponding author (P=0.047), mention of funding (P<0.001), particular host-country involved (P=0.002), journals having sponsor-country editors (P<0.001), and journal stated compliance with International Committee of Medical Journal Editors (ICMJE) guidelines (P=0.003). Logistic regression identified 3 significant factors: mention of funding, journal having sponsor-country editors and research being biomedical. CONCLUSIONS: One-third of articles still do not mention IRB approval. Mention varied by country, and was associated with biomedical research, and more sponsor country involvement. Recently, some journals have required mention of IRB approval, but allow authors to do so in cover letters to editors, not in the article itself. Instead, these data suggest, journals should require that articles document adherence to ethical standards.
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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 | 0.620 | 0.881 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.030 | 0.027 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".