Effects of Eccentric Viewing Training Program on Reading and ADL in Individuals With Central Scotomas: A Single-Subject Research Design
Bibliographic record
Abstract
Objective : To examine the effects of the eccentric viewing training software on reading and Activities of Daily Living(ADL) for individuals with central scotomas in Korea. Methods : A single-subject A-B-A research design for two low-vision individuals with central scotomas was used to examine the effects. The research was conducted over 14 sessions, which included three sessions during a pre-training baseline period (A), eight sessions during an intervention period to apply eccentric viewing training software on Korean character stimulation (B), and three sessions in a post-training baseline period (A). To measure reading and ADL, participants were assessed for their reading speed, Canadian Occupational Performance Measure(COPM), and Assessment of Motor and Process Skills(AMPS). Results : An Analysis of the data showed that the participants’ reading speed increased compared to their pre-training baseline scores, showing the highest level of increase in the first two to three sessions of training. The participants improved their performance in the reading-related activities, although a comparison of their overall ADL function pre-training and post-training showed no change. Conclusion : These findings suggest that the eccentric viewing training based on specific language characters, Korean, is an effective intervention method for improvement in the reading skills of individuals with central scotomas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".