Paediatric vision screening by non-healthcare volunteers: evidence based practices
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to test the sensitivity and specificity of eight undergraduate volunteer examiners conducting vision screening tests in a community setting, in order to determine if non-eye care professionals were able to be trained to an appropriate level of skill. METHODS: Eight undergraduate volunteer examiners were trained to conduct vision screening tests to address a gap in pediatric community eye care. Phase I of the study was implemented in the pediatric ophthalmology clinic, and phase II was conducted in nine local schools. Phase I consisted of 40 h of training for each volunteer regarding specific vision tests. Phase II consisted of screening children at nine local schools. RESULTS: A total of 690 children from nine local schools were screened by both the volunteer examiners and the optometrist during the course of this study. Volunteer examiners had a screening sensitivity of 0.80 (95%CI 0.66-0.90) and screening specificity of 0.75 (95%CI 0.71-0.78) when compared to the study optometrist. The overall accuracy of volunteer examiners was 75%. The resulting positive likelihood ratio was 3.24 (95%CI 2.6-3.9), indicating that a child with vision impairment was 3.2 times more likely to fail the vision test performed by the volunteer examiners compared to a child with no vision impairment. CONCLUSIONS: Non-healthcare professionals can be trained to an acceptable degree of accuracy to perform vision screening tests on children, which may assist in mitigating existing gaps in paediatric eye care.
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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 | 0.032 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".