Sex and Racial/Ethnic Differences in Suicidal Consideration and Suicide Attempts among US College Students, 2011-2015
Bibliographic record
Abstract
Objectives: In this study, we examined sex and racial/ethnic differences in the prevalence and predictors of suicide consideration and attempts among US college students. Methods: We used multivariable logistic regression to investigate suicide consideration and attempts by sex and race/ethnicity among students (N = 319,342) who completed the American College Health Association-National College Health Assessment from fall 2011 to spring 2015. Results: Overall, the prevalence of suicide consideration and attempts was higher in spring 2015 than fall 2011 (p < .05). Men had higher odds of suicide consideration and attempts than women (p < .001). Blacks, Hispanics, and Asians had higher odds of suicide consideration and attempts compared with Whites (p < .001). Weight-related problems (unhealthy weight, body weight overestimation, and lack of physical activity), sleep problems (insufficient sleep and sleep difficulties), and lower levels of academic performance were associated with both suicide consideration and attempts (p < .05). Conclusions: Our findings indicate a need for sex- and race/ethnicity-specific suicide prevention strategies for college students, specifically men and racial/ethnic minority groups. Furthermore, appropriate weight and sleep management could be considered to help prevent suicide among US college students.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".