Prevalence and correlates of substance use by Egyptian school youth
Bibliographic record
Abstract
Loffredo, C., Shaker, Y., Jillson, I., Boulos, D., Saleh, D., Garas, M., Ostrowski, M., Sun, X., Chen, X., Shander, B., & Amr, S. (2017). Prevalence and correlates of substance use by Egyptian school youth. The International Journal Of Alcohol And Drug Research, 6(1), 37-51. doi:http://dx.doi.org/10.7895/ijadr.v6i1.242Aims: Substance use among Egyptian youth is an emerging public health problem, yet there is a paucity of information on the prevalence and correlates of these behaviors. To address this gap, we conducted surveys at 25 schools in Egypt in 2013 and 2014.Design: We calculated associations between substance use prevalence and age, gender, residence area, living arrangement, and employment status, along with adjusted odds ratio (OR) and 95% confidence intervals (CI).Setting: Cairo region and southern Egypt.Participants: School youth ages 12-18 (N=1,415).Measures: Self-administered survey on the use of cigarettes, waterpipes, alcohol, hashish, bango, heroin, Tramadol, other oral medications, injected substances, and glue/petrol sniffing; together with the amount and frequency of each substance used and age at initiation, in addition to demographic characteristics.Findings: Seventy-two percent of participants were male. Tobacco and cannabinoids were the most commonly used substances by both genders. Males reported smoking cigarettes (25%), waterpipes (15%), and hashish (6%), drinking alcohol (16%), and taking Tramadol (3%). Younger age (12–14 years) and residence outside of Cairo were somewhat protective. Among males, but not females, having a job increased the odds of smoking cigarettes (OR = 1.8, 95% CI [1.3, 2.6]), waterpipes (OR = 1.9, 95% CI [1.2, 2.9]), or hashish (OR = 2.0, 95% CI [1.1, 3.7]).Conclusions: These findings, consistent with reports from other countries, can inform the design and direct the resources of future public health programs targeting adolescents to prevent the onset of substance use and ultimately addiction in Egypt and elsewhere.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".