PROBLEMATIC ALCOHOL CONSUMPTION, KNOWLEDGE OF ALCOHOLIC DRINKS AND HEALTH RISKS AMONG FUTURE HEALTH PROFESSIONALS IN IFAKARA, MOROGORO REGION, TANZANIA
Bibliographic record
Abstract
Aims This study was conducted to determine the prevalence of alcohol use disorder, drinking attitude, knowledge of standard drinks and health risks associated with alcohol consumption among health science students in Ifakara town, Morogoro region in Tanzania. Design This study was a cross-sectional survey conducted between June and October 2020 among 372 medicine and allied health students. Participants were selected from the study institutions using random sampling. Results were presented by descriptive statistics, chi- square (χ2) test for associations and logistics regression was used to estimate odds ratios (OR) and 95% confidence (CI) for alcohol use disorder among participants. Results The prevalence of alcohol consumption is 39.2% with males accounting a higher prevalence among drinkers (63.7%). Ages 21-25 accounted for the highest drinking prevalence (65.8% of drinkers). There were associations between drinking and gender (p=0.002), institution of study (p=0.000) and course of study (p=0.000). Ever drinkers were about 41% of respondents. The median age of first consumption is 20.0 (IQR: 17-22). The prevalence rates of AUD (AUDIT score greater than 8) is 16.4%. Women were significantly less likely than men to report AUD (OR=0.22, 95% CI (0.11-0.47), p<0.05). Over 60% of participants did not know the number of standard drinks in commonly sold alcoholic beverages in Tanzania. Conclusion The prevalence of alcohol consumption and hazardous drinking is high among health sciences students. There is also a poor knowledge of standard drinks and recommended drinking limits.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".