State of the eye health system in the Pacific: is medical technology available and used by mid-level eye care workers?
Bibliographic record
Abstract
INTRODUCTION: The aim of this study is to describe the availability, use and comfort with ophthalmic equipment and medications by mid-level eye care workers in Papua New Guinea and Pacific Island countries and territories as indicators of the state of eye care in the Pacific. METHODS: Health information system data, from a workforce support program to Pacific mid-level eye care workers, were analysed for availability and comfort with use of ophthalmic equipment and topical medications. RESULTS: For refraction equipment, access was excellent (98% for retinoscopes and trial lenses) 'very frequent use' range was 42-74% and 'high comfort of use' range was 54-86%. Equipment for ocular health assessment is widely available (slit lamps 67%), with high comfort levels (78-100% 'very comfortable'). Over 70% of respondents have access to topical diagnostic medications, 98% have access to at least one type of antibiotic drops and 63% have access to at least one topical corticosteroid. CONCLUSION: Overall, trained mid-level eye care workers in the Pacific seem well equipped for ocular health and refractive assessments. Comfort levels are encouraging, but also highlight areas for continuing professional development. Access to ophthalmic medications appears acceptable in the region for low morbidity anterior segment conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".