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Record W4390414540 · doi:10.1097/cxa.0000000000000190

Clinical Characterization of Methamphetamine-related Emergency Department Use in a Canadian Psychiatric Hospital

2023· article· en· W4390414540 on OpenAlexaffvenueabout
Alma Rahimi, Nicole Kozloff, Albert H.C. Wong, Kristina M. Gicas

Bibliographic record

VenueThe Canadian Journal of Addiction · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsCentre for Addiction and Mental HealthUniversity of TorontoYork University
Fundersnot available
KeywordsMedicineEmergency departmentSocioeconomic statusEthnic groupMedical recordPsychological interventionLogistic regressionPsychiatryMental healthPopulationFamily medicineEnvironmental health

Abstract

fetched live from OpenAlex

ABSTRACT Objectives: Globally, methamphetamine (MA)-related emergency deparment (ED) visits and hospital admissions have increased. This study examined the characteristics of persons with MA-related ED encounters in a Canadian psychiatric hospital. Methods: A retrospective chart review of ED medical records was conducted between January 2019 and December 2019. Sample characteristics were described using all available 2019 data. Logistic regressions were used to examine predictors of ED visits and hospital admissions. Results: In 2019, there were 659 MA-related ED encounters, of which 438 were unique (single visits=75.6%; admissions=40.9%). Persons were, on average, 34 years old, predominantly male, and homeless. The sample was ethnically diverse, with the largest group identifying as White (58.1%). Psychotic and substance use disorders were common. Younger age and being homeless significantly predicted repeat visits, whereas female gender, non-White ethnicity, psychotic disorder diagnosis, and greater clinical acuity predicted admission. Conclusions: Our findings highlight the multiple intersecting clinical and social dimensions that are associated with more frequent MA-related ED visits and hospital admission. The vulnerable socioeconomic circumstances of this population suggest the need for targeted interventions that address both substance use and mental health concerns from an intersectional perspective to build better pathways to community care. Objectifs: Dans l’ensemble, les visites aux services d’urgence (SU) et les admissions à l’hôpital liées à la méthamphétamine (MA) ont augmenté. Cette étude a examiné les caractéristiques des personnes ayant eu des visites aux SU liées à la MA dans un hôpital psychiatrique canadien. Méthodes: Une étude rétrospective des dossiers médicaux des SU a été réalisée entre janvier 2019 et décembre 2019. Les caractéristiques de l'échantillon ont été décrites à l’aide de toutes les données disponibles de 2019. Des régressions logistiques ont été utilisées pour examiner les prédicteurs des visites aux SU et des admissions à l’hôpital. Résultats: En 2019, il y a eu 659 visites aux SU liées à la MA, dont 438 étaient uniques (visites uniques=75,6% ; admissions=40,9%). Les personnes étaient en moyenne âgées de 34 ans, principalement des hommes et des sans-abri. L'échantillon était ethniquement diversifié, mais le groupe le plus important s’identifiait comme blanc (58,1%). Les troubles psychotiques et les troubles liés à l’utilisation de substances psychoactives étaient fréquents. Le fait d’être jeune et sans-abri prédisaient de manière significative des visites répétées, tandis que le sexe féminin, l’ethnicité non blanche, le diagnostic de trouble psychotique et une plus grande acuité clinique prédisaient l’admission. Conclusions: Nos résultats mettent en évidence les multiples dimensions cliniques et sociales qui s’entrecroisent et qui sont associées à des visites plus fréquentes aux SU et à des admissions à l’hôpital liées à la MA. Les circonstances socio-économiques vulnérables de cette population suggèrent le besoin d’interventions ciblées qui abordent à la fois les problèmes de toxicomanie et de santé mentale dans une perspective intersectionnelle afin d'établir de meilleures voies vers les soins communautaires.

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)0.002
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 score0.281
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.050
GPT teacher head0.382
Teacher spread0.333 · 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

Citations0
Published2023
Admission routes3
Has abstractyes

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