Within-person and between-person associations of access to environmental reward with alcohol and cannabis use and consequences among young adults
Bibliographic record
Abstract
Recent behavioural economic models of substance use suggest that low access to environmental reward may increase risk for heavy substance use and associated harms. Most prior studies of these associations have been cross-sectional and have focused on alcohol. The current study extends this research using longitudinal data to examine the within-person and between-person associations of environmental reward access with both alcohol and cannabis outcomes. Young adults ( N = 119, 64.71 % female) completed an online survey at three time points, spaced six months apart. The survey included measures of alcohol and cannabis use and consequences, and two facets of environmental reward access: reward probability (i.e., likelihood of experiencing environmental reward) and environmental suppression (i.e., diminished availability of environmental reward). Multilevel models revealed that at the between-person level (i.e., averaged across time points), greater environmental suppression (but not reward probability) was significantly associated with more frequent cannabis use, and greater reward probability (but not environmental suppression) was significantly associated with heavier alcohol use. Higher environmental suppression (but not reward probability) was also associated with greater alcohol and cannabis consequences at the between-person level, over and above level of use. A significant within-person association also was observed, wherein participants reported relative increases in cannabis consequences during time periods when they also reported relative decreases in the availability of environmental reward. Results highlight environmental suppression as a risk factor for more frequent cannabis use and for both alcohol and cannabis consequences, and provide novel support for a within-person association between environmental suppression and cannabis consequences over time. Findings may inform contextual interventions for young adult substance use. • Examined within- and between-person associations of environmental reward access with substance use outcomes over one year. • Between persons, greater environmental suppression predicted greater cannabis use frequency and alcohol and cannabis harms. • Between persons, greater reward probability predicted heavier alcohol use. • Within-person increases in environmental suppression predicted corresponding increases in cannabis harms. • Diminished availability of environmental reward is a risk factor for alcohol and cannabis use and harms.
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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 | 0.000 | 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 | 0.001 |
| 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".