Predictors of vision screening among Saudis at primary healthcare settings in Riyadh, Saudi Arabia: findings from a cross-sectional survey
Bibliographic record
Abstract
Background Visual impairment, including low vision and blindness, is an important global health concern. In Saudi Arabia, research on vision screening prevalence and its predictors is limited. This study aimed to determine the prevalence of vision screening and identify associated factors among Saudi residents attending primary healthcare settings. Methods A cross-sectional survey was conducted from March to July 2023, involving 14,239 participants from 48 randomly selected primary healthcare centers in Riyadh. Data were collected electronically from participants aged 18 years and older, using a validated questionnaire covering sociodemographic characteristics, health-related behaviors, and comorbidities. Vision screening (yes/no) was the outcome of interest, and predictors were identified using multiple logistic regression. All statistical analyses were performed using Statistical Package for the Social Sciences (SPSS) software. Results The mean age of the population sample was 59.7 years ± SD 16.6 years, 56.6% were female, and 65.3% were married. The overall prevalence of vision screening was 9.1%. Multivariable analysis revealed that higher education (AOR 0.65–0.67, 95% CI [0.50–0.84] for up to high school; [0.52–0.87] for college/university; [0.44–0.76] for others) and marriage (AOR 0.81, 95% CI [0.70–0.94]) were associated with lower odds of vision screening. Conversely, unemployment (AOR 1.28, 95% CI [1.12–1.46]), exercise (AOR 1.29, 95% CI [1.14–1.47]), diabetes (AOR 1.49, 95% CI [1.24–1.80]), and obesity (AOR 1.39, 95% CI [1.11–1.75]) were associated with higher odds (all p < 0.05). Age, sex, insurance coverage, smoking, and hypertension did not reach statistical significance. Conclusion Overall, the prevalence of vision screening among the Saudi residents was low. This study identified key sociodemographic and health-related predictors of vision screening among Saudi residents. Targeted interventions are needed to improve screening rates, particularly among underutilizing groups such as those with higher education, married individuals, and employed individuals. Future research should qualitatively explore underlying reasons for these disparities to inform effective and culturally sensitive strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".