Research ethics committees in the regulation of clinical research: comparison of Finland to England, Canada, and the United States
Bibliographic record
Abstract
BACKGROUND: The aim of this paper is to compare common features and variation in the work of research ethics committees (RECs) in Finland to three other countries - England, Canada, the United States of America (USA) - in the late 2000s. METHODS: Several approaches and data sources were used, including semi- or unstructured interviews of experts, documents, previous reports, presentations in meetings and observations. A theoretical framework was created and data from various sources synthesized. RESULTS: In Finland, RECs were regulated by a medical research law, whereas in the other countries many related laws and rules guided RECs; drug trials had specific additional rules. In England and the USA, there was a REC control body. In all countries, members were voluntary and included lay-persons, and payment arrangements varied. Patient protection was the main ethics criteria, but other criteria (research advancement, availability of results, payments, detailed fulfilment of legislation) varied. In all countries, RECs had been given administrative duties. Variations by country included the mandate, practical arrangements, handling of multi-site research, explicitness of proportionate handlings, judging scientific quality, time-limits for decisions, following of projects, role in institute protection, handling conflicts of interests, handling of projects without informed consent, and quality assurance research. The division of work between REC members and secretariats varied in checking of formalities. In England, quality assurance of REC work was thorough, fairly thorough in the USA, and not performed in Finland. CONCLUSIONS: The work of RECs in the four countries varied notably. Various deficiencies in the system require action, for which international comparison can provide useful insights.
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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 | MetaresearchResearch integrity Domain: Methods · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.075 | 0.157 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".