Research with adolescents who engage in non-suicidal self-injury: ethical considerations and challenges
Bibliographic record
Abstract
Non-suicidal self-injury (NSSI) has emerged as a significant psychiatric issue among youth. In addition to its high prevalence rates, NSSI is associated with a number of psychiatric issues and confers risk for varying degrees of physical injury. It is also a risk factor for attempted suicide. Thus, youth who engage in NSSI represent a vulnerable and high-risk population and researchers are likely to encounter a variety of ethical challenges when conducting NSSI research. Accordingly, it is critical that researchers be familiar with the major ethical issues involved in NSSI research and how to effectively account for and address them. This is important both prior to obtaining clearance from their Institutional Review Boards and when carrying out their research. To date, there is no consolidated resource to delineate the ethical challenges inherent to NSSI research and how these can be effectively navigated throughout the research process. The goals of this paper are to review international best practices in NSSI research across the various contexts within which it is studied, to offer guidelines for managing these issues, to identify areas in which variation in approaches prohibits decisive recommendations, and to generate questions in need of further consideration among scholars in this field.
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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 | 0.575 | 0.517 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.028 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".