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Record W2568143998 · doi:10.1177/0022042616687118

Self-Perceived Strengths Among Adolescents With and Without Substance Abuse Problems

2017· article· en· W2568143998 on OpenAlexaff
Nicholas Harris, James Brazeau, Edward P. Rawana, Keith Brownlee, Rupert Klein

Bibliographic record

VenueJournal of Drug Issues · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsLakehead University
Fundersnot available
KeywordsSubstance abusePsychologyClinical psychologySubstance useSubstance abuse treatmentStrengths and weaknessesPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

The importance of examining positive aspects of youth development has been emphasized across disciplines involved in the care of youth with substance abuse problems. However, little is known about the strengths of adolescents with substance abuse problems, especially youth entering residential treatment. Utilizing the Strengths Assessment Inventory, a measure assessing psychological and social strengths, we examined patterns of strengths across groups of age- and gender-matched youth who reported no substance use, frequent substance use, and those entering treatment for severe substance use. Each group consisted of 43 participants ranging in age from 14 to 18 years. Results indicated that, on average, individuals entering treatment scored lower on personal strengths. However, through the use of more sophisticated statistical approaches, it was found that certain strengths were predictive of individuals belonging to the treatment group. Results are discussed in terms of their relevance to the treatment of adolescent substance abuse problems.

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)0.001
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 score0.155
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.015
GPT teacher head0.297
Teacher spread0.282 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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