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Record W2164195181 · doi:10.3109/14659890903075066

Comparing outcomes for alcohol and drug abuse clients: A 6-month follow-up of clients who completed a residential treatment programme

2010· article· en· W2164195181 on OpenAlexaff
Janice Hambley, Simone Arbour, Lakshmi Sivagnanasundaram

Bibliographic record

VenueJournal of Substance Use · 2010
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsBell (Canada)
Fundersnot available
KeywordsCannabisAddictionPsychiatryDrugAttendanceMedicineSubstance abuseQuality of life (healthcare)Addiction treatmentMedical prescriptionSubstance abuse treatmentSubstance usePsychologyClinical psychologyPharmacologyNursing

Abstract

fetched live from OpenAlex

The present study examined the impact of an in-patient addiction treatment program and whether client-related treatment outcomes were moderated by addiction type: (1) alcohol only; (2) cocaine only or with alcohol; (3) cocaine with other substances; and (4) prescription drugs and/or cannabis. Clients completed self-reports of their substance use and quality of life during their first week in treatment and at 6-months post-discharge. Pre-treatment motivation and post-treatment aftercare attendance were also assessed. Overall, a positive impact of the addiction treatment programme was noted as clients reported a significant reduction in substance use and improvement in quality of life. Results also demonstrated that drug of choice impacted recovery status such that compared with cocaine poly substance clients, alcohol clients obtained significantly higher scores on quality of life measures at both pre- and 6-months post-treatment. However, cocaine poly substance clients were also significantly younger than alcohol only clients and were less likely to be married or employed. In general, substance use clients responded well to treatment. Some variability was noted among substance use groups—namely that cocaine poly-drug users obtained lowest levels of post-treatment reduction in substance use. The implications of such findings are discussed.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.072
GPT teacher head0.321
Teacher spread0.249 · 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

Citations19
Published2010
Admission routes1
Has abstractyes

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