Wiser Reasoning and Less Disgust Have the Potential to Better Achieve Suicide Prevention
Bibliographic record
Abstract
Abstract. Background: High school and university teachers need to advise students against attempting suicide, the second leading cause of death among 15–29-year-olds. Aims: To investigate the role of reasoning and emotion in advising against suicide. Method: We conducted a study with 130 students at a university that specializes in teachers' education. Participants sat in front of a camera, videotaping their advising against suicide. Three raters scored their transcribed advice on "wise reasoning" (i.e., expert forms of reasoning: considering a variety of conditions, awareness of the limitation of one's knowledge, taking others' perspectives). Four registered psychologists experienced in suicide prevention techniques rated the transcripts on the potential for suicide prevention. Finally, using the software Facereader 7.1, we analyzed participants' micro-facial expressions during advice-giving. Results: Wiser reasoning and less disgust predicted higher potential for suicide prevention. Moreover, higher potential for suicide prevention was associated with more surprise. Limitations: The actual efficacy of suicide prevention was not assessed. Conclusion: Wise reasoning and counter-stereotypic ideas that trigger surprise probably contribute to the potential for suicide prevention. This advising paradigm may help train teachers in advising students against suicide, measuring wise reasoning, and monitoring a harmful emotional reaction, that is, disgust.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".