Health‐Risk Behaviors and Protective Factors Among Adolescents in Rural British Columbia
Bibliographic record
Abstract
PURPOSE: This study explores the relationship between rural residency, selected protective factors (family and school connectedness along with prosocial peer attitudes), and health-compromising behaviors (alcohol and tobacco use and nonuse of seatbelt) among adolescents. METHODS: statistic to test rural-urban differences separately by gender. Logistic regression analysis was used to examine the relationship between protective factors and behaviors compromising health. FINDINGS: In boys, rural residency was associated with multiple problem behaviors (binge drinking, smokeless tobacco use, and nonuse of seatbelt), whereas for girls it was linked to riding without a seatbelt. The final logistic regression models confirmed that rural environment was a significant risk factor for not wearing a seatbelt among both boys and girls, and smokeless tobacco among boys (adjusted odds ratio between 1.44 and 3.05). Rurality, on the other hand, did not predict binge drinking. Logistic regression analyses also revealed that both school connectedness and prosocial peer attitude protected boys against binge drinking and smokeless tobacco, but the results were not as robust for girls. CONCLUSIONS: These findings could provide information for location-based intervention efforts promoting adolescent health, highlighting the protective role of the school atmosphere and prosocial peer relationships, especially among boys.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".