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Record W4220764265 · doi:10.1176/appi.ps.202100206

Treatment of Patients Presenting With Suicidal Behavior in North American Pediatric Emergency Departments

2022· article· en· W4220764265 on OpenAlexaboutno aff
Megan M. Mroczkowski, Alison M. Lake, F. Meridith Sonnett, Saba Chowdhury, Madelyn S. Gould

Bibliographic record

VenuePsychiatric Services · 2022
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSuicidal ideationMental healthMedicineSuicide preventionOccupational safety and healthFamily medicinePoison controlMental health careMedical emergencyEmergency departmentHuman factors and ergonomicsInjury preventionHealth carePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: This study sought to identify current practices for the treatment of patients presenting with suicidal ideation or a recent suicide attempt in pediatric emergency departments (EDs) in North America. METHODS: From October 10, 2018, to January 19, 2020, the authors conducted a cross-sectional online survey on current practices of pediatric emergency medicine chiefs practicing in the United States and Canada. RESULTS: Forty-six (34%) of 136 chiefs of pediatric emergency medicine responded to the survey. The three most frequent improvements chiefs reported they would like to see in the care of young patients with suicidal ideation or suicide attempt were easier access to mental health personnel for evaluations, having mental health personnel take primary responsibility for patient evaluation and treatment, and better access to mental health personnel for dispositional planning. CONCLUSIONS: The findings highlight the need for better mental health care in pediatric EDs to serve patients at increased risk for suicide.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.294
Teacher spread0.278 · 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

Labeled directly by 2 models reading the full record.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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