Effects of word prediction and location of word prediction list on text entry with children with spina bifida and hydrocephalus
Why this work is in the frame
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Bibliographic record
Abstract
AbstractIn this study, a single-subject alternating-treatments design was used to evaluate the effect of word prediction on the rate and accuracy of text entry and to compare the effect of location of a word prediction list on the rate and accuracy of text entry. Three locations were evaluated: upper right corner, following the cursor, and lower middle border. KeyREP© was the word prediction software used in this study. Three girls and one boy aged 10 to 12 years with spina bifida and hydrocephalus participated in the study over a period of 20 days. The rates and accuracy of text entry were measured on a copy-writing task. It was found that word prediction did not improve the rates of text entry but did improve the accuracy of text entry when the prediction list was placed in the lower middle border. Statistically, there was no difference in rate or accuracy when the prediction list was placed in different locations; however, three participants recorded the lowest rate, and all participants achieved lowest accuracy when the prediction list followed the cursor. The findings are discussed in terms of user characteristics, the dictionary used in the software, and the nature of the writing task (copying text) because these are common factors that can affect the effectiveness of word prediction.Keywordsphysical impairment single-subject design software typing rate word list word prediction writing aid
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it