A systematic review of the impact of childhood vision impairment on reading and literacy in education
Bibliographic record
Abstract
PURPOSE: This systematic review evaluates current literature on the impact vision impairment has on reading and literacy levels within education. METHODS: Six databases were searched with inclusion criteria of trials or studies involving children who are blind or vision impaired, and impact on academic or school performance - including reading and literacy. 1262 articles were identified, with 61 papers undergoing full screening. Quality appraisal was performed using Critical Appraisal Skills Program (CASP) and seven articles deemed eligible for inclusion. RESULTS: Included articles achieved a quality score of over 70 % using the CASP checklists. Direct comparison of articles was not possible due to methodological differences in assessing reading and literacy levels. All seven studies investigated aspects of reading speed, with additional measures of reading performance, such as reading reserve, comprehension, and reading accuracy. DISCUSSION: Underlying trends highlighted students with a vision impairment do not perform at same level as their normally sighted peers with respect to reading performance - in terms of speed, but not ability. Additionally, early intervention to enhance literacy skills may help improve educational outcomes. Future direction should be aimed at identifying specific obstacles to learning these students face and providing interventions to improve academic outcomes.
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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".