Incidences of visual disorders on academic difficulties
Bibliographic record
Abstract
Abstract Purpose Many students underestimate their visual discomfort, although it may have an educational impact. We studied the prevalence of visual disorders among students and compared these results to their academic level. Methods Between September 2012 and April 2013, four hundred students between fifteen and twenty two years of age responded to a questionnaire followed by a visual screening (refraction and binocular vision) in order to detect any visual anomalies they might be unaware of. Then academic performance from participants was appraised and subjects were reviewed to determine whether wearing appropriate optical correction or taking orthoptic care improved their grades. Results Methods from multidimensional approaches (principal component analysis) and explanatory approaches (econometrics) were used for data analysis. Results indicate that the questionnaire score is very significant to predict probability of having academic difficulties (79%) or vision problems (89.42%). Refraction error (+16.78%) and accommodation anomalies (+16.21%) have effects on academic difficulties and binocular vision disorders are even more disadvantageous (+21.95%). Moreover, not spontaneously expressing visual discomfort doesn’t mean that there are no visual defects. Conclusion Once controlled by variables known as impacting learning process, a significant proportion of participants academic difficulties are related to vision anomalies. Therefore, screening of vision anomalies among adolescents appears to be necessary, especially if there are academic difficulties. In addition, the questionnaire used in case history seems to be an effective tool in the detection of vision anomalies and should be validated on a larger sample.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".