Self treatment of eye diseases in Malawi
Bibliographic record
Abstract
Self-treatment for eye diseases is very common in most developing countries yet there has been little investigation of such attitudes and practices. In many settings, people do not proceed beyond self-treatment and do not receive care from either traditional healers or Western eye care providers. Visual impairment and blindness can be the result. We conducted population-based survey of use of eye care services and self-treatment in two districts of Malawi. Adults were administered a detailed interview regarding their use of eye care services (Western and traditional as well as self-treatment) and their knowledge and use of traditional eye medicines. Self-treatment was defined as use of either Western or traditional medicines by the individual for their most recent eye condition. Only eye conditions that were considered severe by the study subjects were correlated with treatment options. Interviews were carried out among 800 adults in the study areas. Self-treatment was reported for the last episode of eye disease by 39.8% of the study population. Factors associated with self-treatment included sex, religion and socioeconomic status. Even though 76.8% of the respondents reported treatment from the health center or hospital to be the least expensive option, many opted for self-treatment first. Among those opting for self-treatment 72% used traditional eye medicines. Even among cases that individuals considered to be quite severe (these included cataract, trachoma and conjunctivitis), self-treatment was the option of choice in 22.2% of cases.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".