1254 Identifying Adolescents at Risk for Suicide Using Observed Classroom Behavior Indicative of Excessive Daytime Sleepiness
Bibliographic record
Abstract
Abstract Introduction Suicide is the second leading cause of death among teenagers. Unfortunately, many at-risk adolescents do not seek help, making it challenging for educators and mental health professionals to identify those in need of support. Adolescents at high risk for suicide exhibit higher daytime sleepiness compared to their low-risk counterparts. The present study therefore evaluated the validity of categorizing adolescents into high and low suicide risk groups based on observed classroom behaviors indicative of excessive daytime sleepiness. Methods Participants: 188 adolescents (M= 14.32 years, SD = 1.88; 59 boys). Measures: Suicidality was assessed using Item 9 of the Beck Depression Inventory: Participants responding “I do not have any thoughts of killing myself” were classified as low risk, while those indicating “I have thoughts of killing myself, but I would not carry them out,” “I would like to kill myself,” or “I would kill myself if I had the chance” were classified as high risk. Daytime sleepiness was assessed using the Sleepiness subscale of the School Sleep Habits Survey, which asks respondents to indicate whether they had struggled to remain awake in ten different situations. Results Discriminant function analysis and group classification were used to predict group membership. One discriminant function was extracted, accounting for 100% of the total variance. The function significantly discriminated between the groups (Wilks’ Lambda =.94, χ²(2) = 11.36, p <.003), indicating strong group separation. Falling asleep in a face-to-face conversation with another person and in class were the strongest variables for differentiating between the two groups. Group classification resulted in 82.2% accuracy for predicting the level of adolescent suicidality. Conclusion Classroom behaviors indicative of excessive daytime sleepiness can effectively differentiate between adolescents at high and low risk for suicide. Engaging with students who fall asleep in class may assist educators and clinicians in identifying adolescents at risk for suicide. By recognizing these behaviors, schools can play a pivotal role in early identification and intervention efforts. Understanding the sources of sleepiness and implementing appropriate interventions have the potential to significantly reduce the risk of suicide within this vulnerable population. Support (if any) Charles, Evelyn & Sandra Dolansky Foundation CIHR grant#365284
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".