Factor Structure and Measurement Invariance of the Alcohol Use Disorders Identification Test (AUDIT) in a Sample of Military Veterans with and without PTSD
Bibliographic record
Abstract
Background: The Alcohol Use Disorders Identification Test (AUDIT) was developed as a screening tool for problematic alcohol use and an intervention framework to aid those who drink excessively. While the AUDIT is widely used with at-risk populations, such as military veterans, major gaps exist in the research literature regarding the construct validity of the AUDIT in military samples. Objectives: This study assessed the factor structure and measurement invariance of the AUDIT in a large sample of Canadian military veterans (N = 1669; 94.94% male). Methods: Exploratory factor analysis (EFA) was conducted using a random subsample (n = 825) to assess the underlying factor structure of the AUDIT. Confirmatory factor analysis (CFA), using the second subsample (n = 844), was used to cross-validate the factor structure revealed by EFA and compare it to other model variants. Finally, multigroup CFAs were conducted using the whole sample to further cross-validate the factor structure and examine measurement invariance in military veterans with and without clinical elevations in posttraumatic stress disorder (PTSD) symptoms. Results: Factor analyses revealed that a modified two-factor model provided a statistically better fit to the data compared to all other model variants; yet, the results did not confirm measurement invariance across military veterans with and without clinically significant symptoms of PTSD. Conclusions/Importance: The findings are in line with increasing evidence suggesting that two subscale scores should be calculated for the AUDIT. Results further suggest that care should be taken in interpreting AUDIT scores when PTSD symptoms are present for military veterans.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".