Living environment, social context, heavy drinking and cigarette smoking among university students: an assessment of utilizing an internet survey
Bibliographic record
Abstract
Research has shown that living environment and social context are related to heavy drinking and cigarette smoking. The purpose of this study was to determine the role of living environment and the social context in which heavy drinking and cigarette smoking occur among Canadian university students. The present study utilized a cross-sectional online survey (a) to provide an assessment of alcohol use and smoking among undergraduate university students in a Canadian university, (b) to determine what physical and social characteristics of living environment are associated with the risks of heavy drinking and daily cigarette smoking, and (c) to assess the effectiveness of an online approach to collecting health data and the effect of incentives in improving response rates. A random sample of 2,274 undergraduate students was selected for participation. Students were sent a series of e-mail messages requesting their participation in the online survey. Non-respondents were followed-up by mail. The overall response rate was 61%. Binary logistic regression modeling was used to examine the study's hypotheses regarding living environment and social context factors and the odds of heavy drinking and daily smoking. Logistic results showed that hypotheses related to heavy drinking in the past two weeks and environmental factors of household and neighbourhood composition were supported. Hypotheses regarding heavy drinking the last time alcohol was consumed and the social context factors of planning to drink and smoking cigarettes were supported. Other hypotheses were not supported. The use of an online instrument was found to be a fast, reliable and low-cost method for collecting data. However, overall low response to the online survey made follow-up of non-responders by mail was necessary. Students who received information regarding a prize incentive did not differ significantly in terms of the number of contacts or the number of days needed to complete a survey from students who did not receive prize information. These results suggest that comprehensive programs and interventions that incorporate living environment and social context factors might be useful in reducing the risk of injury and death associated with heavy drinking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".