Clinical measurement of addictions
Bibliographic record
Abstract
INTRODUCTION AND AIMS: To conduct a systematic review of instruments for the clinical measurement of addictive behaviours and to determine whether substance addictive behaviours (SAB) and non-substance addictive behaviours (NSAB) are similarly conceptualised in clinical research. DESIGN AND METHODS: The analytic strategy employed comprised three steps: (i) major search engines were used to take stock of available clinical instruments for assessing addictive behaviours; (ii) an analysis grid was developed and validated, covering 21 parameters under four heuristic categories: dependence, temperament, social handicap and cognitive behaviour; and (iii) all instruments were analysed and compared via the grid. RESULTS: The search yielded 157 questionnaires covering 14 addictive behaviours.The analysis grid allowed rating all questionnaire items on one parameter only; very good interrater agreement was maintained throughout.The categories most evaluated by the questionnaires were dependence and cognitive behaviour; temperament and social handicap were much less frequently considered. Patterns were generally similar in terms of categories, whether questionnaires concerned SAB or NSAB; however, differences within categories indicated a greater frequency of psychologically oriented parameters for NSAB. CONCLUSIONS: The measurement of addictive behaviours appears clinically cohesive, as determined by a validated analysis grid applied to an exhaustive set of questionnaires identified through a systematic literature review.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".