Alcohol use disorder among healthcare professional students: a structural equation model describing its effect on depression, anxiety, and risky sexual behavior
Bibliographic record
Abstract
BACKGROUND: Mental health problems such as depression, anxiety and alcohol use disorders are among the leading causes of disability worldwide. Among university students, alcohol use and poor mental health are associated with risky sexual behavior. Given the syndemic occurrence of these disorders most especially in young adults, we describe the relationship between them so as to guide and intensify current interventions on reducing their burden in this population. METHODS: This was a cross-sectional study based on an online survey among healthcare professional university students that captured sociodemographic characteristics, risky sexual behavior, alcohol use disorder, generalized anxiety disorder, and depression. Structural equation modelling was used to describe the relationship between these variables using RStudio. RESULTS: = 44.437, df = 21, p-value = 0.01, CFI = 0.989, TFI = 0.980, RMSEA = 0.056]. All observed variables were found to fit significantly and positively onto their respective latent factors (AUD, anxiety, depression and risky sexual behavior). AUD was found to be significantly associated with risky sexual behavior (β = 0.381, P < 0.001), depression (β = 0.152, P = 0.004), and anxiety (β = 0.137, P = 0.001). CONCLUSION: AUD, depression and anxiety are a significant burden in this health professional student population and there's need to consider screening for anxiety and depression in students reporting with AUD so as to ensure appropriate interventions. A lot of attention and efforts should be focused on the effect of AUD on risky sexual behavior and continued health education is still required even among health professional students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".