Different drugs come with different motives: Examining motives for substance use among people who engage in polysubstance use undergoing methadone maintenance therapy (MMT)
Bibliographic record
Abstract
BACKGROUND: Substance use motives (i.e., reasons for using a substance) are thought to be the most proximal variable leading to substance use. These motives have been described by various typologies, the most well known being the four-factor drinking motives model which separates motives into enhancement, social, coping, and conformity (Cooper, 1994). Although extensively studied in adult community samples, motives for use have less commonly been investigated among populations at a later stage of addiction, where polysubstance use is more common. Moreover, because the motives literature has largely focused on drinking motives, it is not clear whether existing findings can also be applied to other substances (Cooper et al., 2016). METHODS: Using Zero-inflated beta Bayesian linear mixed modeling, we investigated the stability of seven distinct substance use motives (enhancement, social, expansion, coping with anxiety, coping with depression, coping with withdrawal, and conformity) across six different drug categories (tobacco, alcohol, cannabis, opioids, stimulants, and tranquilisers) to determine the extent to which drug class can influence motive endorsement. One-hundred-and-thirty-eight methadone maintenance therapy (MMT) clients (F = 34.1%; M = 65.9%; age = 40.18 years) completed a novel short-form polysubstance motives questionnaire. RESULTS: External motives (i.e., conformity and social motives) were the most stable across drug categories, while all internal motives (i.e., enhancement, expansion, and all three coping motives) demonstrated varying levels of inter-drug variability. CONCLUSIONS: These findings have important implications for prevention and intervention strategies among people who engage in polysubstance use, highlighting the importance of both universal and substance-specific programming.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".