Prevalence and correlates of cannabis abuse among vulnerable communities following multiple natural disasters
Bibliographic record
Abstract
INTRODUCTION: Most individuals use cannabis for relaxation and may misuse this substance. Vulnerable communities who have experienced multiple traumas may be predisposed to cannabis abuse. Hence, more cannabis abuse is deserving of more attention. OBJECTIVES: To determine the prevalence and correlates of likely cannabis abuse among residents of Fort McMurray. METHODS: A cross-sectional survey design was adopted, employing an online questionnaire. Data were analyzed with SPSS version 25. Correlation analysis was conducted to assess likely cannabis abuse and its association with other mental health conditions. RESULTS: One hundred and eighty-sixed out of the two hundred and forty-nine completed the online survey, giving a response rate of 74.7%. The prevalence of self-reported cannabis abuse was 14%. Most of the participants were females (159, 85.5%), owned their houses (145, 78.0%), and 103 (60.6%) reported being exposed to at least a trauma (COVID-19, flooding, or wildfire). Rented accommodation predicted likely cannabis abuse (OR = 3.86; 95% CI: 1.34–11.14), males were more likely to abuse cannabis than the female gender (OR= 6.25; 95% CI: 1.89–20), and participants in a relationship were more likely to abuse cannabis (OR = 6.33; 95% CI: 1.67–24.39). There was a statistically significant association between depressive and anxiety symptoms and likely cannabis abuse. CONCLUSIONS: The study found an association between depression and anxiety symptoms with cannabis abuse among residents of the Fort McMurray population. Sociodemographic characteristics predispose individuals to problematic cannabis use. Vulnerable communities who have endured multiple disasters need psychological care and support to reduce and prevent cannabis abuse. DISCLOSURE OF INTEREST: None Declared
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".