MétaCan
Menu
Back to cohort

Who Comes Back? Characteristics and Predictors of Return to Emergency Department Services for Pediatric Mental Health Care

2010· article· en· W2011704452 on OpenAlexaffabout
Amanda S. Newton, Samina Ali, David W. Johnson, Christina Haines, Rhonda J. Rosychuk, Rachel A. Keaschuk, Philip Jacobs, Mario Cappelli, Terry P. Klassen

Bibliographic record

VenueAcademic Emergency Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineEmergency departmentConfidence intervalOdds ratioPsychological interventionAmbulatoryLogistic regressionMental healthPediatricsDemographyEmergency medicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Abstract Objectives: The objective of this study was to investigate predictors of emergency department (ED) return visits for pediatric mental health care. The authors hypothesized that through the identification of clinical and health system variables that predict return ED visits, which children and adolescents would benefit from targeted interventions for persistent mental health needs could be determined. Methods: Data on 16,154 presentations by 12,589 pediatric patients (≤17 years old) were examined from 2002 to 2006, using the Ambulatory Care Classification System (ACCS), a provincewide database for Alberta, Canada. Multivariable logistic regressions identified predictors, while survival analyses estimated time to ED return. Results: In the multivariable analysis, there were four patient factors significantly associated with ED return. Male sex (odds ratio [OR] = 0.78; 99% confidence interval [CI] = 0.69 to 0.89) was associated with a lower rate of return, as was child age. The likelihood of ED return increased with age. Children ≤5 years (OR = 0.26; 99% CI = 0.14 to 0.46) and between ages 6 and 12 (OR = 0.64; 99% CI = 0.51 to 0.79) were less likely to return, compared to 13‐ to 17‐year‐olds. Patients with families receiving full assistance for covering government health care premiums were more likely to return compared to those with no assistance (OR = 1.59; 99% CI = 1.33 to 1.91). Patients were more likely to return if their initial presentation was for a mood disorder (OR = 1.72; 99% CI = 1.46 to 2.01) or psychotic‐related illness (OR = 2.53; 99% CI = 1.80 to 3.56). There were two modest health care system predictors in the model. The likelihood of return decreased for patients triaged as nonurgent (OR = 0.62; 99% CI = 0.45 to 0.87) versus those triaged as urgent (level 3 acuity) and increased for patients with visits to general (vs. pediatric) EDs (OR = 1.25; 99% CI = 1.03 to 1.52). ED region (urban vs. rural) did not predict return. Within 72 hours of discharge, 6.1 and 8.7% of patients diagnosed with a mood disorder and psychotic‐related illness, respectively, returned to the ED. Throughout the study period, 28.5 and 36.6% of these diagnostic populations, respectively, returned to the ED. Conclusions: Among children and adolescents who accessed the ED for mental health concerns, being female, older in age, in receipt of social assistance, and having an initial visit for a mood disorder or psychotic‐related illness were associated with return for further care. How patient presentations were triaged and whether visits were made to a pediatric or general ED also affected the likelihood of return. ACADEMIC EMERGENCY MEDICINE 2010; 17:177–186 © 2010 by the Society for Academic Emergency Medicine

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)0.001
metaresearch head score (Gemma)0.005
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 score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.017
GPT teacher head0.334
Teacher spread0.317 · 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

Citations94
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueAcademic Emergency MedicineSame topicEmergency and Acute Care StudiesFrench-language works237,207