Alcohol consumption, mental health status, and treatment in Nigeria and Uganda
Bibliographic record
Abstract
Background: The current level of alcohol consumption has placed Nigeria and Uganda in the group of high consumption countries, however little is known about how people with problematic alcohol use and related problems utilize treatment services. Aims: This study examined the relationship between alcohol consumption and mental health status in Nigeria and Uganda, and the relationship between heavy episodic drinking and treatment-seeking and treatment-receiving behavior. Data and methods: Analyses were based on cross-sectional survey data from Nigeria (N= 2018) and Uganda (N=1478) aged > 18 years from the 2003 Gender, Alcohol, and Culture: An International Study (GENACIS). Results: In both countries, the level of alcohol consumption was comparatively high, however, associations between drinking status and mental health problems were found only in Nigeria. Heavy episodic drinkers were more likely to report having sought help in both countries, only in Nigeria was it also related to ever receiving help. Conclusion: National strategies in both countries must continue allocation of resources to treatment services, supporting treatment availability and early identification of alcohol and related mental health problems. Implementation of national alcohol policies should be followed up with assessment and adjustments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".