(Don't Fear) the Reefer: Cannabis Worldview Beliefs and the Management of Death-Related Existential Concerns Among High Frequency Cannabis Users
Bibliographic record
Abstract
Introduction: The current research employed a terror management framework to understand the cognitive effects of frequent drug use. The study focused specifically on cannabis users and tested the hypothesis that frequent cannabis use is associated with the development of cannabis-related worldview beliefs that take on an existential function for frequent users. Method: Participants (N = 226) answered questions about their cannabis use and completed a measure of cannabis worldview investment. Thereafter, they were randomly assigned to a cannabis worldview threat (vs. no threat) condition and completed measures of death-thought accessibility (DTA) and cannabis worldview defense. Results: A positive association between frequency of cannabis use and cannabis worldview investment was observed. Moreover, among high frequency cannabis users, those highly invested in the cannabis worldview evinced significantly more DTA following exposure to the worldview threat than no threat condition. Participants with high investment in the cannabis worldview also showed more derogation of the cannabis worldview threat (vs. no threat) essay-author. However, this relationship was not influenced by DTA or frequency of cannabis use. Discussion: A terror management perspective on drug use is discussed, including implications for understanding drug abuse, addiction, and treatment.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".