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Record W1981221495 · doi:10.1080/1533256x.2012.728485

Adolescents' Perspectives on Strengths-Based Group Work and Group Cohesion in Residential Treatment for Substance Abuse

2012· article· en· W1981221495 on OpenAlexaffabout
Nicholas Harris, James Brazeau, Ashley Clarkson, Keith Brownlee, Edward P. Rawana

Bibliographic record

VenueJournal of Social Work Practice in the Addictions · 2012
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsCarleton UniversityLakehead University
Fundersnot available
KeywordsGroup cohesivenessPsychologyCohesion (chemistry)Thematic analysisSubstance abuseClinical psychologySubstance abuse treatmentPerceptionQualitative researchPsychotherapistSocial psychology

Abstract

fetched live from OpenAlex

Abstract It is common to conduct treatments for adolescent substance abuse within a group format. Because previous research has identified group cohesion as being an important factor in adolescent substance abuse group treatment outcomes, it is important to examine therapeutic strategies that could help facilitate the development of group cohesion within substance abuse treatment programs for adolescents. The purpose of this study was to examine adolescents' perspectives on the contribution of a strengths-based orientation to the development of group cohesion. Following a 5-week strengths-based residential treatment program for substance abuse in Canada, 36 adolescents agreed to participate in an open-ended interview about their perceptions of how a strengths-based approach might have influenced group cohesion. Thematic analysis was used to identify common themes across participant responses. Findings suggest that working from a strengths-based perspective could help facilitate the development of several aspects of group cohesion. Implications for future research and clinical practice are discussed. Keywords: adolescencegroup cohesionposttreatment interviewsqualitative researchresidential treatment in Canadastrengths perspectivesubstance abuse treatment

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.055
GPT teacher head0.426
Teacher spread0.371 · 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 designQualitative
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

Citations7
Published2012
Admission routes2
Has abstractyes

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