What do eye care workers do when their patients go blind?
Bibliographic record
Abstract
Sometimes even with the best efforts by the eye care worker (ECW), patients cannot be stopped from losing vision even in the best of centers anywhere in the world. However, in developing countries, most vision loss happens in rural and suburban areas away from where ECWs are majorly located due to poor facilities, adverse living conditions, and poverty. Once irreversible blindness happens, rehabilitation should follow. However, the numbers of those who are not referred for rehabilitation by far outstrips those who are, for various reasons. To find out why this is so, 150 ECWs with 1:2 M:F ratio were contacted through Google links sent through WhatsApp groups. Glaucoma was statistically the commonest cause of irreversible blindness (χ 2 = 66.17, p-value < .0001) mostly from late presentation ( n = 146 of 150 responses, 97.7%). When patients go blind, most ( n = 132, 87.4%) of the ECW advise them to go to a blind school (81.2%). Only about a quarter of the respondents properly ensure that they go. A third admitted ( n = 78, 39%) that knowing the patients personally improved their willingness to refer. Many do not think the government is doing enough to help the blind ( n = 118, 78.7%). Even though many ECWs have given sensitization talks on blindness (124 of 164 responses), very few focus on what happens after blindness occurs (42.4% of respondents). A third of the ECW admitted to not doing enough for the blind in their practice ( n = 51, 34%). Majority have, however, heard about The Lens Eye Clinic (TLEC) rehab center, one of the foremost rehabilitation centers for the blind in Nigeria ( n = 103, 68.7%). ECW should ensure those who live in rural areas have poor socioeconomic background, less educated, female, elderly, or born blind should have regular screening and awareness programs in the areas of practice to catch the condition on time with provision made for early counseling and support services.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".