Alcohol use and health care utilization in rural Liberia: Results of a community-based survey for basic public health indicators
Bibliographic record
Abstract
Weil, A., Cameron, C., Soumerai, J., Dierberg, K., Mouwon, A., Kraemer, D., Lewy, D., Lee, P., Kraemer, J., & Siedner, M. (2014). Alcohol use and health care utilization in rural Liberia: Results of a community-based survey for basic public health indicators. The International Journal Of Alcohol And Drug Research, 3(2), 169-181. doi:http://dx.doi.org/10.7895/ijadr.v3i2.147Aim: To measure the association between alcohol use and health-seeking behavior in post-conflict Liberia.Design: Cross-sectional survey.Setting: A community in rural southeast Liberia, from January 11 to January 16, 2010Participants: 600 heads-of-household.Measures: Logistic regression models for estimation of associations between alcohol use and indicators of healthcare utilization. Frequent alcohol use was defined as drinking more than seven days out of the last two weeks.Findings: Frequent alcohol use was reported by 14.9% of participants. These respondents were less likely to attend clinic for chronic cough (Adjusted Odds Ratio, AOR 0.40, 95% CI 0.18-0.87), to have had an HIV test (AOR 0.39, 95% CI 0.19-0.77), and to have accessed prenatal care (AOR = 0.26, 95% CI 0.12-0.54). Approximately 25% of all respondents had no access to latrines, and half reported going to sleep hungry in the past week.Conclusions: Within households in post-conflict Liberia, there is an association between reduced health care utilization and frequent alcohol use self-reported by a head of household or primary caregiver.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".