Examining the Prospective Associations Between Mindfulness Facets and Substance Use in Emerging Adulthood
Bibliographic record
Abstract
AIMS: Emerging adulthood (i.e. ages 18-25) is a developmental phase associated with frequent alcohol and cannabis use, placing this population at risk for substance use problems. Depression and anxiety (i.e. emotional psychopathology) are also prevalent during this phase, and some emerging adults use substances to cope with these negative emotions. Mindfulness-a multifaceted construct-involves being present in a nonjudgmental and nonreactive way. Certain mindfulness facets are particularly relevant in buffering against substance use. A recent longitudinal study [Single A, Bilevicius E, Johnson EA. et al. (2019) Specific facets of trait mindfulness reduce risk for alcohol and drug use among first-year undergraduate students. Mindfulness 10:1269-1279] showed that specific mindfulness facets (i.e. acting with awareness, nonjudging of inner experience and nonreactivity to inner experience) predicted decreased alcohol and drug use in undergraduates. These pathways were explained by low levels of emotional psychopathology. METHODS: This study expanded this recent work by using a three time-point longitudinal design and by including measures of both alcohol and cannabis use and related problems. Using MTurk, participants (N = 299) completed online measures of trait mindfulness, depression, anxiety, alcohol and cannabis use and related problems at three time-points, each 2 weeks apart. Structural equation modeling was used to test the hypotheses. RESULTS: The acting with awareness and nonjudging of inner experience facets predicted fewer alcohol problems, but not alcohol use, and this effect was mediated by low levels of emotional psychopathology. These results were not supported for cannabis use and problems. CONCLUSION: This study demonstrates that there may be differences in the pathways from trait mindfulness to alcohol and cannabis use during emerging adulthood.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".