Ethical Challenges in Promoting the Implementation of Preventive Interventions: Report of the SPR Task Force
Bibliographic record
Abstract
Prevention science researchers and practitioners are increasingly engaged in a wide range of activities and roles to promote evidence-based prevention practices in the community. Ethical concerns invariably arise in these activities and roles that may not be explicitly addressed by university or professional guidelines for ethical conduct. In 2015, the Society for Prevention Research (SPR) Board of Directors commissioned Irwin Sandler and Tom Dishion to organize a series of roundtables and establish a task force to identify salient ethical issues encountered by prevention scientists and community-based practitioners as they collaborate to implement evidence-based prevention practices. This article documents the process and findings of the SPR Ethics Task Force and aims to inform continued efforts to articulate ethical practice. Specifically, the SPR membership and task force identified prevention activities that commonly stemmed from implementation and scale-up efforts. This article presents examples that illustrate typical ethical dilemmas. We present principles and concepts that can be used to frame the discussion of ethical concerns that may be encountered in implementation and scale-up efforts. We summarize value statements that stemmed from our discussion. We also conclude that the field of prevention science in general would benefit from standards and guidelines to promote ethical behavior and social justice in the process of implementing evidence-based prevention practices in community settings. It is our hope that this article serves as an educational resource for students, investigators, and Human Subjects Review Board members regarding some of the complexity of issues of fairness, equality, diversity, and personal rights for implementation of preventive interventions.
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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 | 0.412 | 0.465 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.017 | 0.034 |
| Insufficient payload (model declined to judge) | 0.001 | 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".