Prevalence and predictors of self-reported alcohol abuse and its association with other mental health conditions in the residents of Fort McMurray after multiple traumas
Bibliographic record
Abstract
INTRODUCTION: People consume and abuse alcohol for varied reasons. Problematic alcohol use is associated with mental and physical health risks, while people exposed to multiple traumas may be more vulnerable to abusing alcohol. OBJECTIVES: To evaluate the prevalence and predictors of self-reported alcohol abuse among residents of Fort McMurray and explore the correlates of self-reported alcohol abuse with some mental health conditions. METHODS: A cross-sectional study adopted an online questionnaire. Sociodemographic data, trauma exposure, and clinical characteristics were collected to identify the predictors of self-reported alcohol abuse. Data were analyzed using SPSS version 25 using cross-tabulations and logistic regression analysis. RESULTS: Two hundred and forty-nine individuals received the survey link, of which 186 completed the survey, with a response rate of 74.7%. Most participants were females exposed to COVID-19 and either wildfire or flooding traumas. The prevalence of self-reported alcohol was 27.4%. Participants who desired mental health counselling were likely to self-report alcohol abuse (OR=3.017; 95% CI: 1.349-6.750). There was a significant association between self-reported alcohol abuse and self-rated moderate to high depression (X (2) = 4.783; p = 0.033) and anxiety symptoms (X (2) = 4.102; p = 0.047), and suicidal ideations or thoughts of self-harm (X (2) = 13.536; p = 0.001). CONCLUSIONS: Self-reported alcohol abuse is correlated with suicidal ideations, the desire to receive mental health counselling, and anxiety and depression symptoms. Therefore, initiatives to minimize mental health disorders are crucial to reducing alcohol abuse and promoting health among vulnerable populations. 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".