Perceived benefits of word prediction intervention on written productivity in children with spina bifida and hydrocephalus
Bibliographic record
Abstract
Word prediction has been commonly used as a tool to enhance written productivity. However, the effectiveness of word prediction as a strategy to meet this targeted outcome has not been established. Using a single-subject alternating treatments design, this study evaluated the effect of word prediction on written productivity from the users' perspectives. Three girls and one boy aged 10-12 with spina bifida and hydrocephalus participated in the study over a period of 20 days. The Canadian Occupational Performance Measure (COPM) was used to measure changes in perception of written productivity. Analysis of individual participant data showed that participants perceived word prediction to have the potential to influence written productivity on some writing tasks. Quantitative analysis using a randomization test did not reveal any significant changes in COPM scores after using word prediction. The varied abilities of the participants in the study and the small sample size may be the reasons why statistical analysis did not show any changes. The limitations of this study included use of a copy task, lack of a supporting measure to COPM and limited generalizability. Further studies with a larger sample are necessary to explore the skills required for successful use of word prediction and the impact of word prediction on specific tasks.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".