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Record W4292978391 · doi:10.1097/jhq.0000000000000361

Patient Characteristics and Positive Outcomes Associated With a Novel Youth Inpatient Program for Concurrent Disorders

2022· article· en· W4292978391 on OpenAlexaboutno aff
Luc Saulnier, Kamyar Keramatian, Jordan J. Cohen

Bibliographic record

VenueJournal for Healthcare Quality · 2022
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMental healthPopulationUnit (ring theory)Quality of life (healthcare)PsychiatryPublic healthHealth carePsychologyNursingEnvironmental health

Abstract

fetched live from OpenAlex

ABSTRACT: Concurrent substance use and mental health disorders in youth are a major public health concern and require specialized and comprehensive services. In this paper, a novel inpatient tertiary care facility serving youth aged 13 to 18 with significant concurrent substance use and mental health issues is introduced. The development of this unit was prompted by the opioid overdose crisis in British Columbia and serves as the third concurrent disorders unit in Canada catered specifically to an adolescent population. From its opening in 2017, preadmission and postadmission data from each patient was gathered with the aim of providing a robust image of the serviced patient population as well as the efficacy of this service model. Patients admitted to this program had significantly higher quality of life ( d = 0.65) and significantly lower suicidality ( d = 0.86) at discharge, compared with at admission. Patients identifying as female had significantly lower quality of life, higher suicidality, and higher prevalence of adverse childhood events compared with patients identifying as male. Results from this program evaluation outline the efficacy of a novel concurrent disorders program for youth while further providing an overview of clinical and relevant demographic characteristics from an underanalyzed patient population.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.066
GPT teacher head0.391
Teacher spread0.325 · 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
Published2022
Admission routes1
Has abstractyes

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