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Record W4390483628 · doi:10.1080/07347324.2023.2299254

Addiction Treatment Outcomes: Examining the Impact of an Inpatient Program for Substance Use Disorders and Concurrent Mental Distress

2024· article· en· W4390483628 on OpenAlexaffabout
Lindsey A. Snaychuk, Samantha R. Pejic, Tisha J. Ornstein, Christina Anne Basedow

Bibliographic record

VenueAlcoholism Treatment Quarterly · 2024
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAddictionDistressAnxietySubstance useMental healthPsychiatryClinical psychologyDepression (economics)PsychologyAffect (linguistics)Substance abuseMedicine

Abstract

fetched live from OpenAlex

Substance use disorders (SUDs) affect over 35 million individuals worldwide and are associated with significant harms. Residential treatment is an essential component of Canada’s approach to combating SUDs, offering highly intensive and specialized acute services. Historically, residential treatment facilities have quantified the success of their program(s) solely based on program completion. However, there has been a recent movement toward empirically evaluating programs using standardized measures of distress. The purpose of this study was to evaluate whether the inpatient residential program for SUDs at Edgewood Treatment Centre is effective in providing substantive improvement of a range of addiction and mental health symptoms. The current study assessed both addiction related and psychological outcomes. Specifically, we examined whether patients improved on various measures of functional impairment, addiction-related symptoms, traumatic stress, and psychological distress, following completion of the 7-week inpatient addiction program. Findings suggested that there were significant improvements in emotion regulation, and decreases in substance dependence, substance cravings, anxiety, depressive symptoms, and traumatic stress between admission and discharge.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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 head0.051
GPT teacher head0.358
Teacher spread0.307 · 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.

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

Citations2
Published2024
Admission routes2
Has abstractyes

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