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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".