Access to medical records for research purposes: varying perceptions across research ethics boards
Bibliographic record
Abstract
INTRODUCTION: Variation across research ethics boards (REBs) in conditions placed on access to medical records for research purposes raises concerns around negative impacts on research quality and on human subject protection, including privacy. AIM: To study variation in REB consent requirements for retrospective chart review and who may have access to the medical record for data abstraction. METHODS: Thirty 90-min face-to-face interviews were conducted with REB chairs and administrators affiliated with faculties of medicine in Canadian universities, using structured questions around a case study with open-ended responses. Interviews were recorded, transcribed and coded manually. RESULTS: Fourteen sites (47%) required individual patient consent for the study to proceed as proposed. Three (10%) indicated that their response would depend on how potentially identifying variables would be managed. Eleven sites (38%) did not require consent. Two (7%) suggested a notification and opt-out process. Most stated that consent would be required if identifiable information was being abstracted from the record. Among those not requiring consent, there was substantial variation in recognising that the abstracted information could potentially indirectly re-identify individuals. Concern over access to medical records by an outside individual was also associated with requirement for consent. Eighteen sites (60%) required full committee review. Sixteen (53%) allowed an external research assistant to abstract information from the health record. CONCLUSIONS: Large variation was found across sites in the requirement for consent for research involving access to medical records. REBs need training in best practices for protecting privacy and confidentiality in health research. A forum for REB chairs to confidentially share concerns and decisions about specific studies could also reduce variation in decisions.
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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 | 0.089 | 0.164 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".