Bibliographic record
Abstract
Emerging adults (ages 18-29) have the highest rates of cannabis use and associated consequences relative to other age groups. Cannabis motives (i.e., reasons for using cannabis) are one of the most established predictors of cannabis outcomes, however, the literature is limited in the following ways: (1) there has been no knowledge synthesis conducted on the increasing number of studies, (2) very limited research has evaluated personality risk factors which play a key role in understanding individual differences in motivations for cannabis use, and (3) majority of studies are cross-sectional. The proposed dissertation consists of three studies that focus on addressing these gaps. Study one is a scoping review that summarizes existing studies looking at cannabis motives among emerging adults and their connection to cannabis-related outcomes. The scoping review identified 45 studies and highlighted coping and enhancement motives as the most relevant among this age group for their connection to cannabis outcomes. Study two examined cannabis motives as a mediator in the relationship between two personality risk factors (i.e., anxiety sensitivity, sensation seeking) and cannabis consequences. Findings from this study uncovered two motives that explain the relationship between these personality factors and consequences – coping motives (e.g., to relieve low mood) influenced the relationship between anxiety sensitivity and consequences, and altered perception motives (e.g., to think differently) influenced the relationship between sensation seeking and consequences. Study three used ecological momentary assessment to capture cannabis motives and consequences over time, and tested whether anxiety sensitivity and sensation seeking might moderate this relationship. Analyses revealed that anxiety sensitivity moderated the relationship between social anxiety motives and consequences, and sensation seeking moderated the relationship between enjoyment motives and consequences as well as between altered perception motives and consequences. This study highlights anxiety sensitivity and sensation seeking as important predictors of problematic cannabis consequences over time at the momentary level, especially when endorsing negative reinforcement or positive reinforcement motives (respectively). Overall, these studies will serve to support prevention efforts and treatment protocols for emerging adults who are the most vulnerable to the harms associated with problematic cannabis use.
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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".