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 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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".