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Record W3004520774 · doi:10.5993/ajhb.44.2.9

Sex and Racial/Ethnic Differences in Suicidal Consideration and Suicide Attempts among US College Students, 2011-2015

2020· article· en· W3004520774 on OpenAlexaff
Jaesin Sa, Ches Siyoung Choe, Ches Beom-Young Cho, Jean‐Philippe Chaput, Jounghee Lee, Sungjae Hwang

Bibliographic record

VenueAmerican Journal of Health Behavior · 2020
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsEthnic groupLogistic regressionOddsMedicineSuicide preventionDemographyOdds ratioCollege healthInjury preventionPoison controlOccupational safety and healthGerontologyPsychologyEnvironmental healthFamily medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.079
GPT teacher head0.402
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations46
Published2020
Admission routes1
Has abstractyes

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