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Record W7161982803 · doi:10.82308/40084

Factors associated with cannabis use frequency, cannabis craving, and cannabis-related problems among adults with chronic pain

2023· dissertation· en· W7161982803 on OpenAlexaboutno aff
Connie Colagrosso

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsCannabisLogistic regressionCravingChronic painRecreational drug useAddictionYoung adultDrug

Abstract

fetched live from OpenAlex

Background: Many adults report using cannabis for the relief of chronic pain. However, the regular use of cannabis, especially high-frequency use, might be accompanied by problems in some persons with chronic pain. To date, the person-specific factors (e.g., sociodemographic, clinical, psychological) that contribute to high-frequency cannabis use among persons with chronic pain remain unclear. Little is also known on the person-specific factors that are associated with cannabis-related problems in this population.Objectives: The first objective of the present thesis was to examine the person-specific factors associated with cannabis use frequency among individuals using cannabis for chronic pain. The second objective was to examine the factors associated with cannabis craving among individuals using cannabis for chronic pain. The third objective was to examine the factors associated with cannabis-related problems in this population.Methods: This study included cross-sectional data from the Canadian Tobacco Alcohol and Drugs Surveys (CTADS) and included adults (n = 395) who reported using cannabis for chronic pain. Structured phone interviews were conducted to assess a host of sociodemographic, clinical, psychological, substance use, and cannabis-related variables.Results: A multivariable logistic regression analysis indicated that men, tobacco smokers, and recreational drug users were significantly more likely to be frequent cannabis users (all p's < .05). A univariate logistic regression analysis also indicated that recreational drug users were significantly more likely to be frequent cannabis cravers than non-users (B = .658, OR = 1.931, p = .039). However, in a subsequent multivariable regression analysis, recreational drug use was no longer significantly associated with cannabis craving and neither were any of the other variables (all p’s > .05). Results from a series of univariate analyses indicated that men, younger individuals, recreational drug users, prescription drug misusers, and frequent cannabis users were more likely to experience cannabis-related problems (all p's < .05). Finally, results from a multivariable logistic regression analysis indicated that men (B = .895, OR = 2.448, p = .004) and frequent cannabis cravers were significantly more likely to experience cannabis-related problems than infrequent cannabis cravers (B = .723, OR = 2.062, p = .014).Conclusions: Findings reported in the present thesis provide valuable new insights into our understanding of person-specific factors that may contribute to cannabis use frequency, cannabis craving, and cannabis-related problems in persons using cannabis for the management of chronic pain. These findings might have implications for clinicians involved in the management of persons with chronic pain who are using cannabis. Findings from the present thesis might ultimately contribute to the prevention or reduction of cannabis-related harms among persons with chronic pain

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.260
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

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