Can one simple questionnaire assess substance‐related and behavioural addiction problems? Results of a proposed new screener for community epidemiology
Bibliographic record
Abstract
BACKGROUND AND AIMS: There is currently no well-validated measure that assesses a broad spectrum of substance-related and behavioural addictions in general populations. This study aimed to develop a brief self-attribution Screener for Substance and Behavioural Addictions (SSBA) to screen for four substances and six behaviours, and to compare its performance with established individual-behaviour screening instruments. DESIGN: A small, psychometrically optimal set of items to assess self-attributed indicators of addiction across alcohol, tobacco, cannabis, cocaine, gambling, shopping, videogaming, overeating, sexual activity and overworking were identified from a broader pool that was developed using a lay epidemiology qualitative approach. The suitability of the four-item single-factor solution was tested for each behaviour and scores were compared with those obtained from the sample using individual-behaviour screening instruments. SETTING AND PARTICIPANTS: Participants (n = 6000), broadly representative of the Canadian English-speaking adult population, were recruited through the Ipsos Reid Canadian Online Panel. MEASUREMENTS: Participants completed an item pool of 15 indicators of addiction for each target behaviour and a validation instrument for one randomly assigned behaviour. FINDINGS: A set of four items identified using principal component and confirmatory factor analyses demonstrated good fit and excellent internal consistency (α = 0.87-0.95) across behaviours, and good convergent validity (rs = 0.44-0.8) with extant instruments measuring similar constructs, with only one exception (r = 0.26). CONCLUSIONS: The proposed Screener for Substance and Behavioural Addiction is a reliable and valid measure assessing the lay public's self-attributed indicators of addiction across 10 substances and behaviours.
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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 | 0.055 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".