Assistive Technology and Handwriting Problems: What do Occupational Therapists Recommend?
Bibliographic record
Abstract
BACKGROUND: Handwriting difficulties for students are a common reason for referral to occupational therapy. Little research evidence is available concerning the factors guiding technology recommendations for these children. PURPOSE: The objective of this survey research was to describe the technology-related recommendations and factors involved in the decisions made by Canadian occupational therapists for these students. RESULTS: More therapists recommended the use of keyboard-based strategies (93%) than dictation-based strategies (72%). Experienced therapists were more likely to prescribe technology tools. Dictation to a scribe (93%) and desktop computers (89%) were the strategies most frequently recommended. Equipment cost and availability of funding, and the availability of support in the school for the student were the most influential factors, respectively, on the keyboard and dictation strategy type prescribed. PRACTICE IMPLICATIONS: The results confirmed that occupational therapists prescribe a range of technology solutions. Factors influencing these recommendations differ depending on the nature of the technology, the person, environment or occupation. Knowing the factors guiding occupational therapist technology recommendations will help provide valuable information about the practical implications of the available technologies.
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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 | 0.006 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".