What makes public health studies ethical? Dissolving the boundary between research and practice
Bibliographic record
Abstract
BACKGROUND: The generation of evidence is integral to the work of public health and health service providers. Traditionally, ethics has been addressed differently in research projects, compared with other forms of evidence generation, such as quality improvement, program evaluation, and surveillance, with review of non-research activities falling outside the purview of the research ethics board. However, the boundaries between research and these other evaluative activities are not distinct. Efforts to delineate a boundary - whether on grounds of primary purpose, temporality, underlying legal authority, departure from usual practice, or direct benefits to participants - have been unsatisfactory.Public Health Ontario has eschewed this distinction between research and other evaluative activities, choosing to adopt a common framework and process to guide ethical reflection on all public health evaluative projects throughout their lifecycle - from initial planning through to knowledge exchange. DISCUSSION: The Public Health Ontario framework was developed by a working group of public health and ethics professionals and scholars, in consultation with individuals representing a wide range of public health roles. The first part of the framework interprets the existing Canadian research ethics policy statement (commonly known as the TCPS 2) through a public health lens. The second part consists of ten questions that guide the investigator in the application of the core ethical principles to public health initiatives.The framework is intended for use by those designing and executing public health evaluations, as well as those charged with ethics review of projects. The goal is to move toward a culture of ethical integrity among investigators, reviewers and decision-makers, rather than mere compliance with rules. The framework is consonant with the perspective of the learning organization and is generalizable to other public health organizations, to health services organizations, and beyond. SUMMARY: Public Health Ontario has developed an ethics framework that is applicable to any evidence-generating activity, regardless of whether it is labelled research. While developed in a public health context, it is readily adaptable to other health services organizations and beyond.
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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 | 0.645 | 0.563 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.032 | 0.299 |
| Scholarly communication | 0.073 | 0.053 |
| Open science | 0.011 | 0.037 |
| Research integrity | 0.035 | 0.040 |
| 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".