Investigating mental representations of psychoactive substance use and other potentially addictive behaviors using a data driven network-based clustering method
Bibliographic record
Abstract
BACKGROUND AND AIMS: The aim of the present study was to examine the mental representations of the use of different substances and other potentially addictive behaviors in order to explore meaningful similarities and differences that may contribute to a better understanding of behavioral addictions' representations and diagnostic criteria. METHODS: The authors mapped the mental and emotional representations of 661 participants (70.5% women; Mage = 35.2 years, SD = 11.7) to the concept "your most disturbing excessive activity" using free-word associations combined with a network-based clustering method. RESULTS: The network analyses identified four distinct mental representations, three implicating dominantly negative (Guilt/Shame/Relief, Addiction/Health, and Procrastination/Boredom) and one dominantly positive emotion (Stress/Relaxation). The distribution of Addiction/Health and Procrastination/Boredom representations were different across substance use and problem behaviors, indicating meaningful differences in the underlying cognitive evaluation processes. The Addiction/Health representation was more frequent for substances, while for other addictive behaviors, the Procrastination/Boredom representation was more frequent, and its frequency increased with the self-reported intensity of the behavior. Guilt/Shame/Relief was equally common for both substances and behaviors, but importantly, for substances its' likelihood increased with the intensity of use. CONCLUSION: The common part of representations for substance use and other potentially addictive behaviors supports the scientific viewpoint, that real addictions can exist even in the absence of psychoactive drugs. Based on the results, a novel proposition is posited, that a more appropriate indicator of tolerance for problem behaviors might be the perceived amount of time wasted on the activity rather than the actual time spent.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 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".