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Record W63441433 · doi:10.1177/070674371205701004

Emergency Department Visits and Use of Outpatient Physician Services by Adults with Developmental Disability and Psychiatric Disorder

2012· article· en· W63441433 on OpenAlexafffundvenueabout
Yona Lunsky, Elizabeth Lin, Rob Balogh, Julie Klein-Geltink, Andrew S. Wilton, Paul Kurdyak

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2012
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsInstitute for Clinical Evaluative SciencesCentre for Addiction and Mental Health
FundersOntario Ministry of Health and Long-Term CareInstitute for Clinical Evaluative SciencesCentre for Addiction and Mental Health
KeywordsEmergency departmentMedicinePsychiatryMental illnessPopulationPrimary careOutpatient clinicAmbulatory careMental healthFamily medicineHealth care

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare the emergency department (ED), primary, and psychiatric care visit rates associated with the presence and absence of a developmental disability (DD) and a mental illness. METHOD: This is a population-based study comparing Ontario adults, with and without DDs and mental illnesses, in terms of rates of primary, psychiatric, and ED care, from April 2007 to March 2009. RESULTS: In Ontario, 45% of adults with a DD received a psychiatric diagnosis during a 2-year period, and 26% of those with a psychiatric diagnosis were classified as having a serious mental illness (SMI), compared with 8% of those with a psychiatric diagnosis but no DD. People with DDs had an increased likelihood of psychiatric and ED visits. Patients with SMIs and DDs had the highest rates of such visits. CONCLUSIONS: People with more severe impairments had the greatest likelihood of ED visits, despite access to outpatient services, suggesting that outpatient care (primary and psychiatric), as currently delivered, may not be adequate to meet their complex needs.

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 score0.105
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.015
GPT teacher head0.262
Teacher spread0.247 · 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

Citations79
Published2012
Admission routes4
Has abstractyes

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