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Record W47904203

A Description of Emergency Care Received by Children and Youth with Mental Health Presentations for Alcohol and Other Drug use in two Alberta Emergency Departments.

2010· article· en· W47904203 on OpenAlexaffabout
Andrea Y Yu, Nicole Ata, Kathryn Dong, Amanda S. Newton

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMental healthMedicineEmergency departmentMedical prescriptionStratified samplingMoodMedical recordPsychiatryFamily medicineDescriptive statisticsMedical emergencyNursing
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: This paper describes patient and treatment characteristics of pediatric mental health Emergency Department (ED) visits associated with alcohol and other drug (AOD) use. METHOD: A medical record and administrative database review was conducted. Proportional allocation random stratified sampling identified a representative sample of pediatric (≤18 years) mental health presentations to two tertiary care EDs between April 2004 and March 2006. Descriptive statistics were used to summarize data from 161 patients with associated AOD use. RESULTS: More females (56.5%) and youth aged 15 to 18 years (70.8%) attended the ED for mental health complaints associated with AOD use. Alcohol (48.4%) and over-the-counter or prescription medications (25.5%) were the most commonly used substances. Twenty-four percent of patients had a documented psychiatric history. The most common psychiatric assessments provided were for suicidality (31.1%) and mood (18.0%). Brief counselling was provided in 31.7% of visits. Consultation with psychiatry occurred less than 20% of the time. Most patients were discharged from the ED (65.2%). Sixty-eight percent of patient records did not have documented discharge planning. CONCLUSIONS: When youth present to the ED for mental health concerns related to AOD use, mental health assessments and follow-up care are not occurring in all cases and reasons for this oversight need to be explored.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.632
Threshold uncertainty score0.731

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.300
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2010
Admission routes2
Has abstractyes

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