Psychosocial Factors and Beliefs Related to Intention to Not Binge Drink Among Young Adults
Bibliographic record
Abstract
AIMS: The objective of the study was to identify psychosocial factors and salient beliefs associated with the intention of young people to not binge drink in the next month, applying an extended version of the theory of planned behavior. METHODS: Among 200 youths randomly recruited from adult education centers in the province of Quebec, Canada, 150 completed a questionnaire. Of these, 141 youths reported having used alcohol in the last year-analyses were performed on this sub-sample. RESULTS: The prediction model demonstrated that perceived behavioral control (odds ratio, OR = 2.60, 95% confidence interval, CI 1.59-4.23; P = 0.0001), attitude (OR = 2.49, 95% CI 1.14-5.43; P = 0.02) and moral norm (OR = 1.88, 95% CI 1.23-2.88; P = 0.004) are three determinant variables of intention to not binge drink in the next month. The intention is also related to cannabis use in the last month (OR = 0.17 95% CI 0.05-0.53; P = 0.002). Young people who believe that if they do not binge drink in the next month, they will have a lower risk of getting depressed (OR = 1.53, 95% CI 1.23-1.90; P = 0.0001), and those who believe they will be able to not binge drink even if they are at a party (OR = 1.58, 95% CI 1.29-1.94; P < 0.0001), are more likely to have a positive intention. CONCLUSION: Despite some methodological limitations, this study revealed several options for helping young people to not binge drink during their school career.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".