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Record W4200110933 · doi:10.1027/0227-5910/a000835

Passive Suicidal Ideation in Childhood

2021· article· en· W4200110933 on OpenAlexaff
Lisa Van Hove, Imke Baetens, Karla Van Leeuwen, Mathieu Roelants, J. Roeljan Wiersema, Stephen P. Lewis, Nancy L. Heath

Bibliographic record

VenueCrisis · 2021
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsMcGill UniversityUniversity of Guelph
Fundersnot available
KeywordsSuicidal ideationImpulsivityAggressionLogistic regressionPsychologyClinical psychologySuicide preventionLongitudinal studyPsychiatryMedicinePoison controlMedical emergency

Abstract

fetched live from OpenAlex

Abstract: Background: A growing body of empirical research shows that suicidal behaviors are prevalent in childhood. Yet, few studies have examined risk factors related to suicidal ideation (SI) among children aged 12 and younger. Aims: The current study addresses this gap. Method: A questionnaire was filled out by 1,350 Flemish primary caregivers (94.7% mothers) of 9-year-old children (50.4% boys, M age = 9.45). Their responses were analyzed using logistic regression and independent samples t tests. Results: The presence of passive SI was reported in 10.5% of the children. A psychiatric, developmental, or behavioral condition (or multiple conditions), a learning disorder, impulsivity, aggression, and experiencing multiple stressful family life events were discovered as potential risk factors of passive SI in childhood. Limitations: The cross-sectional nature of this study meant that causality could not be inferred. In addition, it was based on reports of primary caregivers, rather than on reports from the children themselves. Conclusion: These new empirical findings can be used for the development of prevention programs and be taken into account in risk assessments of SI in clinical practice. Confirmation of our findings in a longitudinal child-reported study is needed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.001

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.

Opus teacher head0.022
GPT teacher head0.313
Teacher spread0.291 · 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
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

Citations8
Published2021
Admission routes1
Has abstractyes

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