Beyond Research Ethics: Novel Approaches of 3 Major Public Health Institutions to Provide Ethics Input on Public Health Practice Activities
Bibliographic record
Abstract
Public health institutions increasingly realize the importance of creating a culture in their organizations that values ethics. When developing strategies to strengthen ethics, institutions will have to take into account that while public health research projects typically undergo thorough ethics review, activities considered public health practice may not be subjected to similar oversight. This approach, based on a research-practice dichotomy, is increasingly being criticized as it does not adequately identify and manage ethically relevant risks to those affected by nonresearch activities. As a reaction, 3 major public health institutions (the World Health Organization, US Centers for Disease Control and Prevention, and Public Health Ontario) have implemented mechanisms for ethics review of public health practice activities. In this article, we describe and critically discuss the different modalities of the 3 approaches. We argue that although further evaluation is necessary to determine the effectiveness of the different approaches, public health institutions should strive to implement procedures to ensure that public health practice adheres to the highest 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 | 0.193 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.019 | 0.063 |
| Scholarly communication | 0.033 | 0.028 |
| Open science | 0.006 | 0.041 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".