Pattern of Substance Use: Study in a De-addiction Clinic
Bibliographic record
Abstract
OBJECTIVES: Substance use disorders have become a major public health problem in Bangladesh. We sought to assess the pattern of substance use and related factors among hospitalized patients. METHODS: This was a descriptive study that included 105 patients. All patients who were admitted to a private drug de-addiction clinic in Dhaka, Bangladesh, between 1 July and 31 December 2013 and diagnosed with substance use disorder were enrolled in the study. Data was collected via face-to-face interviews using a semi-structured questionnaire and the information was complemented by the case-notes. RESULTS: Almost all (90.5%) respondents were male and were poly-substance users (91.4%). The mean age of respondents was 28.8±8.0 years. Most (27.6%) respondents used three types of substances. Smoking or inhalation was the route used by most (90.5%) respondents. More than three-fourths (81.0%) of respondents used nicotine. Among the other substances, the majority (79.0%) used opioids, followed by cannabinoids (55.2%), and alcohol (41.0%). Curiosity, peer pressure, and for fun were identified as the common reasons for initiating substance use. CONCLUSIONS: A high proportion of poly-substance use was found in the study population. Our findings could help in the management and development of prevention strategies for substance use in Bangladesh.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".