Testing the healthy immigrant effect on youth alcohol use: A longitudinal study
Bibliographic record
Abstract
Purpose The healthy immigrant effect (HIE) is a phenomenon observed in developed countries in which recent immigrants report better health compared to the majority population. The purpose of the present study was to examine the HIE by comparing two measures of alcohol use over time across three adolescent groups of differing immigration statuses. Methods We examined the HIE by comparing alcohol use quantity and drinking onset longitudinally from grades 7-11 across 1.5, 2 nd , and 3 + immigrant generation status youth ( N =2621-3353). Overall, 38.10% participants completed 5 waves, 30.08% completed 4 waves, 12.86% completed 3 waves, 9.13% completed 2 waves, and 9.27% completed one wave. Results Significant differences were found between immigration generation statuses consistent with the HIE: individuals of 1.5 immigrant generation status reported lower drinking quantity and later onset drinking compared to individuals of 2 nd and 3 + immigrant generation status. Additional analyses revealed socioeconomic status (SES) and alcohol norms to be predictors of alcohol use. Differences in immigrant youth alcohol use quantity (but not onset) may be partially explained by group differences in SES and alcohol norms. Conclusion Results suggest recent immigration status is protective against alcohol use in youth. Future research should examine moderators to the HIE including country-of-origin.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".