Measuring the Outcomes of Word Cueing Technology
Bibliographic record
Abstract
BACKGROUND: Measurement of assistive technology outcomes is complex because many factors (e.g., environment and model of service delivery) influence the successful use of the technology. PURPOSE: Using the example of measuring the outcomes of word cueing technology, this paper presents an approach for measuring assistive technology outcomes. METHOD: The Canadian Occupational Performance Measure (COPM) was administered to 29 children with physical and learning disabilities, between the ages of 3.9 and 19 years. Participants were provided with WordQ, a software program designed to assist the development of writing skills. Follow-up data were collected through telephone interviews. RESULTS: The COPM findings supported the effectiveness of WordQ Version 1 to enhance written productivity, with a mean performance change score of 3.5 (SD = 1.5). The COPM was an effective tool for measuring clients' perceived outcome of word cueing technology. Telephone interview was considered a successful method for collecting outcome data. PRACTICE IMPLICATIONS: A mix of tools and methodologies should be used to gain a comprehensive understanding of the impact of assistive technology.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".