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Record W2010249588 · doi:10.1081/ja-100106960

ALCOHOL AND DRUG ABUSERS' PERCEIVED REASONS FOR SELF-CHANGE IN CANADA AND SWITZERLAND: COMPUTER-ASSISTED CONTENT ANALYSIS

2001· article· en· W2010249588 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSubstance Use & Misuse · 2001
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsCentre for Addiction and Mental Health
FundersNational Institute on Alcohol Abuse and AlcoholismBundesamt für Gesundheit
KeywordsContent analysisPsychological interventionPsychologyCognitionSubstance useClinical psychologyApplied psychologyPsychiatry

Abstract

fetched live from OpenAlex

Although many people recover from substance-use associated problems on their own, little is known about this phenomenon. The paper had two objectives: to use a new research method, computer-assisted content analysis, to understand alcohol and drug abusers' perceived reasons for self-change and to undertake a comparative evaluation across substances and cultures to validate previous findings about subjective appraisal processes. Three studies of natural recoveries of alcohol and drug abusers in two countries conducted tape-recorded interviews with 216 respondents. The taped responses were coded based on a content analytic dictionary approach using a computerized content analysis program. All three studies found several processes mediating the decision to change substance use. The computer content analysis confirmed a cognitive appraisal process regardless of the cultural setting or substance. The findings suggest that several procedures might have benefit in clinical interventions.

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.

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 score0.625
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.066
GPT teacher head0.284
Teacher spread0.218 · 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