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Record W2171985492 · doi:10.5498/wjp.v4.i4.120

Antecedents and sex/gender differences in youth suicidal behavior

2014· review· en· W2171985492 on OpenAlexafffund
Anne E. Rhodes

Bibliographic record

VenueWorld Journal of Psychiatry · 2014
Typereview
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsInstitute for Clinical Evaluative Sciences
FundersCanadian Institutes of Health Research
KeywordsPsychological interventionPsychologyPsychopathologyDevelopmental psychologySuicide preventionPoison controlClinical psychologyPsychiatryMedicineMedical emergency

Abstract

fetched live from OpenAlex

Suicide is the second leading cause of death in youth globally; however, there is uncertainty about how best to intervene. Suicide rates are typically higher in males than females, while the converse is true for suicide attempts. We review this "gender paradox" in youth, and in particular, the age-dependency of these sex/gender differences and the developmental mechanisms that may explain them. Epidemiologic, genetic, neurodevelopmental and psychopathological research have identified suicidal behaviour risks arising from genetic vulnerabilities and sex/gender differences in early adverse environments, neurodevelopment, mental disorder and their complex interconnections. Further, evolving sex-/gender-defined social expectations and norms have been thought to influence suicide risk. In particular, how youth perceive and cope with threats and losses (including conforming to others' or one's own expectations of sex/gender identity) and adapt to pain (through substance use and help-seeking behaviours). Taken together, considering brain plasticity over the lifespan, these proposed antecedents to youth suicide highlight the importance of interventions that alter early environment(s) (e.g., childhood maltreatment) and/or one's ability to adapt to them. Further, such interventions may have more enduring protective effects, for the individual and for future generations, if implemented in youth.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.538
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.088
GPT teacher head0.374
Teacher spread0.286 · 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.

Study designObservational
Domainnot available
GenreReview

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

Citations120
Published2014
Admission routes2
Has abstractyes

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