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Record W3037453938 · doi:10.1521/jscp.2020.39.4.315

(Don't Fear) the Reefer: Cannabis Worldview Beliefs and the Management of Death-Related Existential Concerns Among High Frequency Cannabis Users

2020· article· en· W3037453938 on OpenAlexaff
Joseph Hayes, Jackie Rafferty

Bibliographic record

VenueJournal of Social and Clinical Psychology · 2020
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsAcadia University
Fundersnot available
KeywordsCannabisPsychologyAddictionPsychiatryCognitionExistentialismClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.077
GPT teacher head0.408
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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