Physical and Psychosocial Challenges as Predictors of Vision Difficulty in Children: A Nationally Representative Survey Analysis
Bibliographic record
Abstract
Purpose To elicit associations between vision difficulties and physical or psychosocial challenges in children in the United States.Methods Children aged 2–17 years old from the 2021 National Health Interview Survey with data pertaining to vision difficulty were included in our retrospective, population-based analysis. Our primary aim was investigating physical and psychosocial challenges as predictors of vision difficulty. Logistic regression models were performed on Stata version 17.0 (StataCorp LLC, College Station, Texas). Analyses were accompanied by an odds ratio (OR) and 95% confidence interval (CI).Results A total of 7,373 children had data pertaining to their level of vision difficulty and were included in our sample. In our multivariable analysis, children with a good/fair (OR = 1.84, 95% CI = [1.31, 2.60], p < 0.01), or poor (OR = 5.08, 95% CI = [1.61, 16.04], p < 0.01) general health status had higher odds of vision difficulty relative to children with an excellent/very good health status. Furthermore, children with difficulties hearing (OR = 8.67, 95% CI = [5.25, 14.31], p < 0.01), communicating (OR = 1.96, 95% CI = [1.18, 3.25], p < 0.01), learning (OR = 1.93, 95% CI = [1.27, 2.93], p < 0.01), and making friends (OR = 1.94, 95% CI = [1.12, 3.36], p = 0.02) had higher odds of vision difficulty. Nonetheless, the following factors were only predictors of vision difficulty in our univariable analysis: requiring equipment for mobility (p < 0.01), experiencing anxiety (p < 0.01), and experiencing depression (p < 0.01).Conclusion Several factors pertaining to physical and psychosocial challenges in children are associated with vision difficulty. Future research should further explore potential causal links between vision difficulty and physical or psychosocial factors to aid in coordinating public health efforts dedicated to vision health equity.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".