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Record W2895831642 · doi:10.46743/2160-3715/2018.3512

Exploring Intersecting Program Elements in Longer-Term Concurrent Disorder Services for Adults: A Qualitative Evaluation

2018· article· en· W2895831642 on OpenAlexaff
Aaron Turpin, Micheal L. Shier

Bibliographic record

VenueThe Qualitative Report · 2018
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsQualitative researchPsychologyFocus groupService (business)Qualitative propertyProgram evaluationMedical educationApplied psychologyElement (criminal law)Process managementPsychotherapistMedicineComputer scienceSociologyEngineering

Abstract

fetched live from OpenAlex

Previous research highlights multiple factors that impact the attainment of client-identified recovery goals in substance misuse treatment programs. However, fewer studies examine how programs meet the broad range of needs expressed by clients through their intersecting elements of service delivery. This study seeks to develop an understanding of intersecting program and recovery elements in relation to an overall framework for programming, focusing on how overlapping elements of treatment ventured to support clients in multiple areas of their recovery. Qualitative interviews were conducted with clients (n=41) in three longer term substance use treatment programs, and data from interviews were analysed using analytic induction and constant comparison strategies to surface emergent themes. Data analysis yielded six main findings. These included: Education; Goal Setting; Routine and Stability; Spiritual Development; Exercise; and Transitional Planning. Respondents indicated that programs must focus on bolstering the development of each element across multiple treatment domains (such as group therapy and counselling) to best support clients in achieving recovery outcomes.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.362
GPT teacher head0.560
Teacher spread0.199 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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