Social judgments of behavioral versus substance-related addictions: A population-based study
Bibliographic record
Abstract
Background Recently, the concept of addiction has expanded to include many types of problematic repetitive behaviors beyond those related to substance misuse. This trend may have implications for the way that lay people think about addictions and about people struggling with addictive disorders. The aim of this study was to provide a better understanding of how the public understands a variety of substance-related and behavioral addictions. Methods A representative sample of 4000 individuals from Alberta, Canada completed an online survey. Participants were randomly assigned to answer questions about perceived addiction liability, etiology, and prevalence of problems with four substances (alcohol, tobacco, marijuana, and cocaine) and six behaviors (problematic gambling, eating, shopping, sexual behavior, video gaming, and work). Results Bivariate analyses revealed that respondents considered substances to have greater addiction liability than behaviors and that most risk factors (moral, biological, or psychosocial) were considered as more important in the etiology of behavioral versus substance addictions. A discriminant function analysis demonstrated that perceived addiction liability and character flaws were the two most important features differentiating judgments of substance-related versus behavioral addictions. Perceived addiction liability was judged to be greater for substances. Conversely, character flaws were viewed as more associated with behavioral addictions. Conclusions The general public appreciates the complex bio–psycho-social etiology underlying addictions, but perceives substance-related and behavioral addictions differently. These attitudes, in turn, may shape a variety of important outcomes, including the extent to which people believed to manifest behavioral addictions feel stigmatized, seek treatment, or initiate behavior changes on their own.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".