Alcohol Consumption Behavior among Undergraduate Students in Thailand: Development of a New Causal Relationship Model
Bibliographic record
Abstract
Alcohol consumption among undergraduate students in Thailand is problematic. The aim of this study was to deepen our understanding of this problem by developing a causal relationship model for the alcohol consumption behavior of undergraduate students in Thailand, and to verify the model’s concordance with empirical data. Four latent variables were considered: alcohol consumption behavior, alcohol expectancy, drinking refusal self-efficacy, and health literacy. Participants included representative 1st – 5th year undergraduate students at the Thailand National Sports University, with 600 students being selected using stratified random sampling procedures. The descriptive statistics and the causal relationship model were analyzed using LISREL 8.80. The model developed was in good agreement with the empirical data (c2=228.66, df = 79, p > 0.05 , c2/df=2.894, SRMR =0.07, RMSEA =0.06, CFI =0.99, and RFI =0.98), with all computed indices passing the stipulated criteria. On the basis of the coefficients of determination in the structural equation model, alcohol expectancy, drinking refusal self-efficacy, and health literacy together accounted for 80% of the variance in the student’s alcohol consumption behavior. These theoretically based causal factors provide new directions for future intervention work aimed at modifying the alcohol consumption behaviors of undergraduate students at the Thailand National Sports University. This can be accomplished by developing activities that are suitable and contextually sensitive to their needs.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".