Ethical considerations for monitoring and responding to suicide risk in research studies
Bibliographic record
Abstract
In this lesson, upper-year undergraduate psychology students (or early graduate students) learn the concept of balancing risks/harms with potential benefits in research ethics, as described in the “Tri-Council Policy Statement on Ethical Conduct for Research Involving Humans” (TCPS2 2022). Students consider how this key ethical concept applies to research involving people with histories of suicidal thoughts and/or behaviors. Students learn the practical impacts that researchers’ obligations to mitigate risks have on research assessments and procedures. The lesson is usually taught over two classes. It is best suited to an upper-year undergraduate psychology seminar focused on suicide. This lesson is also most easily integrated into courses that use a “flipped classroom” model wherein students view lecture material and do readings outside of class time and in-person class is reserved for discussions and activities. The lesson assumes that the students will have had a general introduction to research on suicidal behavior.Part 1 of the lesson will introduce common ethical considerations for suicide research, with a particular focus on researchers’ obligation to balance risks and potential benefits in these studies. This will include differentiating between “research attributable risk” and “general risk.” Part 1 will use sections of the TCPS2 2022 directly, as well as literature on how researchers and research ethics boards perceive risks and benefits in studies focused on suicide.Part 2 of the lesson will discuss how risk is assessed in suicide research. The class focuses on the validity of the tools that are often used in suicide studies, and the ethical implications of using approaches that lack predictive validity. Part 2 will use lived experience perspectives on suicide risk assessments to inform considerations of balancing the risks/harms of these assessments with obligations surrounding participant safety, and the benefits of conducting the study.
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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 | 0.068 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".