Substance abuse behaviors among university freshmen in Iran: a latent class analysis
Bibliographic record
Abstract
OBJECTIVES: Substance abuse behaviors among university freshmen in Iran are poorly understood. This study aimed to identify, for the first time, subgroups of university freshmen in Iran on the basis of substance abuse behaviors. Moreover, it examined the effects of socio-demographic characteristics on membership in each specific subgroup. METHODS: Data for the study were collected cross-sectionally in December 2013 and January 2014 from 4 major cities in Iran: Tabriz, Qazvin, Karaj, and Khoramabad. A total of 5,252 first-semester freshmen were randomly selected using a proportional cluster sampling methodology. A survey questionnaire was used to collect data. Latent class analysis (LCA) was performed to identify subgroups of students on the basis of substance abuse behaviors and to examine the effects of students' socio-demographic characteristics on membership in each specific subgroup. RESULTS: The LCA procedure identified 3 latent classes: the healthy group; the hookah experimenter group; and the unhealthy group. Approximately 82.8, 16.1, and 2.1% of students were classified into the healthy, hookah experimenter, and unhealthy groups, respectively. Older age, being male, and having a family member or a close friend who smoked increased the risk of membership in classes 2 and 3, compared to class 1. CONCLUSIONS: Approximately 2.1% of freshmen exhibited unhealthy substance abuse behaviors. In addition, we found that older age, being male, and having a close friend or family member who smoked may serve as risk factors for substance abuse behaviors.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".