Improving Teacher Awareness of Fine Motor Problems and Occupational Therapy: Education Workshops for Preservice Teachers, General Education Teachers and Special Education Teachers in Canada.
Bibliographic record
Abstract
Students with fine motor problems can benefit from occupational therapy. Yet not all students receive the services because of a lack of teacher awareness about the problems and the services. This study aims to evaluate a workshop designed to improve teacher awareness about fine motor problems and occupational therapy. The study involved three groups: preservice (N = 34), general education (N = 30), and special education (N = 19) teachers. Each group received a 2 1⁄2to 3-hour interactive workshop. They completed the Fine Motor Awareness Scale (FMAS) before, after, and one month following the workshops. Preservice teachers had the greatest learning needs on the topic. All three teacher groups showed significant improvements in the FMAS scores post-workshop, with the greatest change in the preservice teachers group, followed by the special education and then the general education teachers. Knowledge transfer principles contributed to the success of the workshops. Post-workshop evaluation showed teachers wanted more content and longer, multi-session workshops in future. Preservice, general and special education teachers need to know more about fine motor problems and occupational therapy. Knowledge-transfer workshops provided by occupational therapists can meet their learning needs and subsequently help their students to improve fine motor problems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".