Knowledge, Attitudes, and Practices of Optometrists Regarding Low Vision Services in Saudi Arabia
Bibliographic record
Abstract
Purpose: To address the human resources challenge for the provision of low vision services in Saudi Arabia, this study sought to investigate the knowledge, attitudes, and practices of optometrists in Saudi Arabia regarding low vision services. The knowledge and attitudes to low vision services can influence the provision of low vision services by optometrists. Methods: A prospective cross-sectional survey of optometrists practicing in Saudi Arabia was undertaken using an online questionnaire designed to elicit the opinions of respondents. The online questionnaire was sent out to optometrists on the official mailing list of registered optometrists and those on the mailing list of the Saudi Society of Optometry. Results: Only 26.5% of the respondents correctly indicated the correct designation of low vision in terms of visual acuity. Although 95.8% indicated that optical low vision devices could help people with low vision, 81.6% reported that low vision devices were expensive, and 42.9% felt low vision practice was not profitable. Only 10.4% of respondents provide low vision services in their practice. Insufficient training in low vision care was the main barrier militating against the provision of low vision services. Conclusion: The pertinent finding in this study is that about a quarter of the respondents could correctly designate low vision in terms of visual acuity using the World Health Organization (WHO) definition. The study concluded that there was poor knowledge, attitudes, and practices of optometrists in Saudi Arabia regarding low vision, which has implications for the provision of low vision services by optometrists.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".