Skills of general health workers in primary eye care in Kenya, Malawi and Tanzania
Bibliographic record
Abstract
BACKGROUND: Primary eye care (PEC) in sub-Saharan Africa usually means the diagnosis, treatment, and referral of eye conditions at the most basic level of the health system by primary health care workers (PHCWs), who receive minimal training in eye care as part of their curricula. We undertook this study with the aim to evaluate basic PEC knowledge and ophthalmologic skills of PHCWs, as well as the factors associated with these in selected districts in Kenya, Malawi, and Tanzania. METHODS: A standardized (26 items) questionnaire was administered to PHCWs in all primary health care (PHC) facilities of 2 districts in each country. Demographic information was collected and an examination aimed to measure competency in 5 key areas (recognition and management of advanced cataract, conjunctivitis, presbyopia, and severe trauma plus demonstrated ability to measure visual acuity) was administered. RESULTS: Three-hundred-forty-three PHCWs were enrolled (100, 107, and 136 in Tanzania, Kenya, and Malawi, respectively). The competency scores of PHCW varied by area, with 55.7%, 61.2%, 31.2%, and 66.1% scoring at the competency level in advanced cataract, conjunctivitis, presbyopia, and trauma, respectively. Only 8.2% could measure visual acuity. Combining all scores, only 9 (2.6%) demonstrated competence in all areas. CONCLUSION: The current skills of health workers in PEC are low, with a large per cent below the basic competency level. There is an urgent need to reconsider the expectations of PEC and the content of training.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".