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 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) | 0.000 | 0.000 |
| 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".