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Record W4405810820 · doi:10.1080/14659891.2024.2446915

Association between pysychiatric symptoms and disability among people with drug dependence in the Community-based Rehabilitation

2024· article· en· W4405810820 on OpenAlexaff
T. Yu, Jiaxin Wu, Qing Zhang, Xinliang Chen, Zongwei Ma, Peng Zhao, Bocheng Chen, Guoxiang Wang

Bibliographic record

VenueJournal of Substance Use · 2024
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsCentre for Drug Research and Development
Fundersnot available
KeywordsRehabilitationAssociation (psychology)Therapeutic communityPsychiatryPsychologyDrugCommunity-based rehabilitationCommunity integrationMedicineClinical psychologyPhysical therapyPsychotherapist

Abstract

fetched live from OpenAlex

Background This study explores the severity of psychiatric symptoms and functional impairments in people with drug dependence undergoing Community-based Rehabilitation (CBR), aiming to understand their impact on social reintegration.Methods From Wuxi City’s CBR population, 164 individuals (98 males and 66 females) were evaluated using the Symptom Checklist-90-Revised for psychotic symptoms and the WHO Disability Assessment Schedule (WHODAS2.0) for functional status. Correlation and multiple regression analyses were conducted to analyze the relationships between educational levels, symptom severity, and disability.Results Significant correlations were found between educational levels, General Symptom Index (138.12 ± 48.45), and WHODAS2.0 scores (28.39 ± 17.59), with psychiatric symptoms notably influencing disability scores (p < .01). Multiple regression highlighted obsessive-compulsive, phobic anxiety, and hostility as significant factors affecting disability domains.Conclusion People with drug dependence in CBR exhibit considerable psychiatric symptoms and disabilities, with symptom severity adversely affecting functional abilities. Enhancing psychiatric interventions in CBR may improve their functional outcomes and facilitate societal reintegration.

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 score0.010
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.017
GPT teacher head0.284
Teacher spread0.266 · 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
Published2024
Admission routes1
Has abstractyes

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