Prevalence of traditional eye medicine and self-treatment in Gurage Zone, Rural Ethiopia
Bibliographic record
Abstract
INTRODUCTION: Traditional medicines are commonly used worldwide, especially in Africa-however, there is limited information on the prevalence and types of traditional eye medicine utilization in Ethiopia. The goal of this study was to determine the prevalence, the type and nature of traditional eye medicine use and practices related to self-medication for ophthalmic diseases in a rural Ethiopian population. METHODS: A cross-sectional study was conducted in six randomly selected primary health centers in rural Gurage Zone, Southern Ethiopia. Health-seeking behavior, use of self-medication, and traditional eye medicine were assessed in the population using a semi-structured questionnaire. Descriptive statistics and multivariable logistic regression analysis were computed to determine associated factors for using self-medication and traditional eye medicine. RESULT: Of the 814 participants interviewed, 487 (59.8%) reported using traditional eye medicine, mainly for combinations of symptoms of ocular redness, irritation, and eye discharge (95.5%). Besides, 604 (74.2%) participants reported self-treatment with tetracycline 1% eye ointment. Older age, females, low income, no formal education, and lack of access to media were risks for utilizing traditional eye medicine. CONCLUSION: The use of traditional eye medicine and self-treatment are common in this population. Regulatory legislation, public awareness, and making eye care are vital activities required to monitor such practices.
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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 | 0.000 | 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".