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Record W2989025850

Relationship between Bullying and Suicidal Behaviour in Youth presenting to the Emergency Department.

2017· article· en· W2989025850 on OpenAlexaffabout
Nazanin Alavi, Taras Reshetukha, Eric Prost, Kristen Antoniak, Charmy Patel, Saad Sajid, Dianne Groll

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsQueen's University
Fundersnot available
KeywordsSuicidal ideationEmergency departmentPsychiatrySuicide preventionVerbal abuseClinical psychologyMedicinePsychologyComplaintPoison controlMedical emergency
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: Increasing numbers of adolescents are visiting emergency departments with suicidal ideation. This study examines the relationship between bullying and suicidal ideation in emergency department settings. METHOD: A chart review was conducted for all patients under 18 years of age presenting with a mental health complaint to the emergency departments at Kingston General or Hotel Dieu Hospitals in Kingston, Canada, between January 2011 and January 2015. Factors such as age, gender, history of abuse, history of bullying, type and time of bullying, and diagnoses were documented. RESULTS: 77% of the adolescents had experienced bullying, while 68.9% had suicide ideation at presentation. While controlling for age, gender, grade, psychiatric diagnosis, and abuse, a history of bullying was the most significant predictor of suicidal ideation. Individuals in this study who reported cyber bullying were 11.5 times more likely to have suicidal ideation documented on presentation, while individuals reporting verbal bullying were 8.4 times more likely. CONCLUSIONS: The prevalence of bullying in adolescent patients presenting to emergency departments is high. The relationship found between suicidal ideation and bullying demonstrates that clinicians should ask questions about bullying as a risk factor for suicide ideation during the assessment of children and adolescents.

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 score0.007
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.091
GPT teacher head0.329
Teacher spread0.238 · 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

Citations47
Published2017
Admission routes2
Has abstractyes

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