Transdiagnostic or Disorder Specific? Indicators of Substance and Behavioral Addictions Nominated by People with Lived Experience
Bibliographic record
Abstract
Using a transdiagnostic perspective, the present research examined the prominent indicators of substance (alcohol, cocaine, marijuana, tobacco) and behavioral (gambling, video games, sex, shopping, work, eating) addictions nominated by people with lived experiences. Specifically, we aimed to explore whether the perceived most important indicators nominated were consistent across the 10 addictions or differed based on the specific addiction. Additionally, we explored gender differences in the perceived most important indicators across addictive behaviors. A large online sample of adults recruited from a Canadian province (n = 3503) were asked to describe the most important signs or symptoms of problems with these substances and behaviors. Open-ended responses were analyzed among a subsample of 2603 respondents (n = 1562 in the past year) who disclosed that they had personally experienced a problem with at least one addiction listed above. Content analyses revealed that dependence (e.g., craving, impairments in control) and patterns of use (e.g., frequency) were the most commonly perceived indicators for both substance and behavioral addictions, accounting for over half of all the qualitative responses. Differences were also found between substance and behavioral addictions regarding the proportion of the most important signs nominated. Consistent with the syndrome model of addiction, unique indicators were also found for specific addictive behaviors, with the greatest proportion of unique indicators found for eating. Supplemental analyses found that perceived indicators across addictions were generally gender invariant. Results provide some support for a transdiagnostic conceptualization of substance and behavioral addictions. Implications for the study, prevention, and treatment of addictions are discussed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".