A risk screening tool for ethical appraisal of evidence-generating initiatives
Bibliographic record
Abstract
BACKGROUND: The boundaries between health-related research and practice have become blurred as initiatives traditionally considered to be practice (e.g., quality improvement, program evaluation) increasingly use the same methodology as research. Further, the application of different ethical requirements based on this distinction raises concerns because many initiatives commonly labelled as "non-research" are associated with risks to patients, participants, and other stakeholders, yet may not be subject to any ethical oversight. Accordingly, we sought to develop a tool to facilitate the systematic identification of risks to human participants and determination of risk level across a broad range of projects (e.g., clinical research, laboratory-based projects, population-based surveillance, and program evaluation) and health-related contexts. This paper describes the development of the Public Health Ontario (PHO) Risk Screening Tool. METHOD: Development of the PHO Risk Screening Tool included: (1) preparation of a draft risk tool (n = 47 items); (2) expert appraisal; (3) internal stakeholder validation; (4) external validation; (5) pilot testing and evalution of the draft tool; and (6) revision after 1 year of testing. RESULTS: A risk screening tool was generated consisting of 20 items organized into five risk domains: Sensitivity; Participant Selection, Recruitment and Consent; Data/Sample Collection; Identifiability and Privacy Risk; and Commercial Interests. The PHO Risk Screening Tool is an electronic tool, designed to identify potential project-associated risks to participants and communities and to determine what level of ethics review is required, if any. The tool features an easy to use checklist format that generates a risk score (0-3) associated with a suggested level of ethics review once all items have been completed. The final score is based on a threshold approach to ensure that the final score represents the highest level of risk identified in any of the domains of the tool. CONCLUSIONS: The PHO Risk Screening Tool offers a practical solution to the problem of how to maintain accountability and appropriate risk oversight that transcends the boundaries of research and practice. We hope that the PHO Risk Screening Tool will prove useful in minimizing the problems of over and under protection across a wide range of disciplines and jurisdictions.
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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 | 0.414 | 0.705 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.047 | 0.022 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".