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Record W2479469009 · doi:10.1080/0886571x.2016.1204257

Emerging Adults Post Discharge from Residential Treatment: Subgroup Profiles of Substance Use

2016· article· en· W2479469009 on OpenAlexafffund
Michèle Preyde, Graham Ashbourne, Randy Penney, Jeff Carter, Karen Frensch, Gary Cameron

Bibliographic record

VenueResidential Treatment for Children & Youth · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsWilfrid Laurier UniversityVanier CollegeYouth Services Bureau of OttawaUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSubstance useYoung adultPsychologyHarmPsychiatryClinical psychologyMedicineGerontologyDevelopmental psychology

Abstract

fetched live from OpenAlex

The outcomes of youth who accessed Residential Treatment are varied; many do not fare well in early adulthood. The purpose for this report was to compare subgroups of substance use behavior among emerging adults who had accessed RT as a child/adolescent on life domains and symptoms gathered by interviews and gleaned from agency files. Of the 59 emerging adults, 11 were categorized as having persistent substance use, 19 as moderate use and 26 as no/minimal use. The mean age at the time of the interview was 19.96 years (SD 1.84), and most (n= 33; 61%) were male. By discharge, the persistent substance use group had significantly greater impairment in functioning in school, home, and self-harm subscales and overall functioning than the other two groups. Fewer participants in the persistent group were employed as emerging adults, and a greater number were self-medicating compared to the other two groups. These results suggest that youth who accessed RT with substance use concerns continue to have difficulties as emerging adults with functioning, symptom severity and life domains. An exploration of specialized programs to address these difficulties appears warranted.

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 score0.075
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.040
GPT teacher head0.344
Teacher spread0.305 · 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
Published2016
Admission routes2
Has abstractyes

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