Patterns and predictors of cigarette smoking among HIV-infected patients in northern Nigeria
Bibliographic record
Abstract
The smoking behaviour of persons living with HIV/AIDS in sub-Saharan Africa is poorly documented. We employed a cross-sectional study design to assess the prevalence and predictors of tobacco smoking among HIV-infected patients in northern Nigeria (n = 296). Approximately one quarter of respondents were either current (7.8%) or ex-smokers (17.9%). Smoking rates among HIV-infected women were extremely low. HIV-infected men were at least three times as likely to smoke as their female counterparts living with HIV: adjusted odds ratio (AOR) 3.16, 95% confidence interval (95% CI) 2.17-7.32. Patients with tertiary education were at least twice as likely to smoke compared with their counterparts without formal education (AOR 2.63, 95% CI 1.08-6.67). The preponderance of cigarette smoking among educated HIV-infected men in northern Nigeria offers a unique opportunity for targeted smoking cessation programmes.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".