Expressions of anger during advising on life dilemmas predict suicide risk among college students
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research has demonstrated a relationship between anger and suicidality, while real-time authentic emotions behind facial expressions could be detected during advising hypothetical protagonists in life dilemmas. This study aimed to investigate the predictive validity of anger expressions during advising for suicide risk. Besides advising on life dilemmas (a friend's betrayal, a friend's suicide attempt), 130 adults completed the suicidal scale of the Mini-International Neuropsychiatric Interview. Participants' anger during advice-giving was measured 29 times/s by artificial intelligence (AI)-based software FaceReader 7.1. The results showed that anger was a significant predictor of suicide risk. Increased anger during advising was associated with higher suicide risk. In contrast, there was no significant correlation between suicide risk and duration or length of advising. Therefore, measuring micro expressions of anger with AI-based software may help detect suicide risk among clinical patients in both traditional and online counseling contexts and help prevent suicide.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it