Demographic, Cannabis Use, and Depressive Correlates of Cannabis Use Consequences in Regular Cannabis Users
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Regular cannabis users experience cannabis-related consequences across many domains of functioning. The present study examined demographic, cannabis use, and depressive correlates of cannabis consequences. We hypothesized that (1) earlier onset of use would predict greater psychological and functional consequences; and (2) women would endorse more psychological and withdrawal consequences. METHODS: Data were collected from an urban sample of 184 adults who reported regular cannabis use. Seventeen items from a cannabis consequence checklist were grouped into three domains: Psychological Consequences, Cannabis Withdrawal, and Functional Consequences. Three multiple regressions were performed to explore demographic and cannabis use correlates of each domain. Correlations between domains and depressive symptoms were assessed using Pearson's r. RESULTS: Greater endorsement on the Psychological Consequence subgroup was predicted by female sex, lower educational attainment, and treatment-seeking history for cannabis abuse/dependence. Individuals with greater number of quit attempts or treatment-seeking history endorsed more items in the Cannabis Withdrawal domain. Although the model failed to reach significance for Functional Consequences, age at onset of regular and daily cannabis use were negatively associated with this domain. Correlational analyses demonstrated higher Beck Depression Inventory-Second Edition scores were related to greater endorsement of Psychological Consequence and Cannabis Withdrawal items. DISCUSSION AND CONCLUSIONS: Regular cannabis users report consequences of use, which can be grouped into content-specific subgroups. Individual characteristics are differentially associated with these subgroups. SCIENTIFIC SIGNIFICANCE: Understanding which individual characteristics are related to cannabis use sequelae could help identify those at risk for greater consequences, thus leading to improved assessment and treatment interventions. (Am J Addict 2019;28:295-302).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".