MétaCan
Menu
Back to cohort
Record W3008353190 · doi:10.1176/appi.ps.201900466

Urgent Outpatient Care Following Mental Health ED Visits: A Population-Based Study

2020· article· en· W3008353190 on OpenAlexaffabout
Lucy C. Barker, Nadiya Sunderji, Paul Kurdyak, Vicky Stergiopoulos, Alejandro Gonzalez, Alexander Kopp, Simone N. Vigod

Bibliographic record

VenuePsychiatric Services · 2020
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsWaypoint Centre for Mental Health Care
Fundersnot available
KeywordsEmergency departmentMedicineMental healthPsychiatrySchizophrenia (object-oriented programming)Bipolar disorderPopulationOutpatient clinicSubstance abuseAmbulatory careHealth careMood

Abstract

fetched live from OpenAlex

Objective: Follow-up after psychiatric emergency department (ED) contact is key to optimizing outcomes for vulnerable patients. We aimed to quantify the likelihood of receiving outpatient mental health care after psychiatric ED visits in a population-level sample. Methods: Among individuals who presented for a psychiatric ED visit in Ontario, Canada (2010–2012) and were not admitted to hospital (N=143,662), the authors estimated the likelihood of outpatient physician mental health care within 14 days post-ED visit and compared this across presenting diagnoses. Results: About 40.2% (N=57,797) had a follow-up mental health visit within 14 days post-ED. Follow-up was lower among individuals presenting with substance use disorders (25.2%) than among those presenting with disorders not primarily related to substance use (44.5%) (χ2=3,784.7, df=1, p<0.001). Follow-up differed among those presenting with schizophrenia (46.4%), bipolar disorder (56.1%), and major depressive disorder (51.1%) (χ2=61.7, df=2, p<0.001). Conclusions: Post-ED outpatient mental health follow-up is low. Systemwide coordination is needed to connect these high-acuity patients with care, especially those with presentations related to substance use.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.306
Teacher spread0.293 · 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

Citations27
Published2020
Admission routes2
Has abstractyes

Explore more

Same venuePsychiatric ServicesSame topicEmergency and Acute Care StudiesFrench-language works237,207