Development and evaluation of an online professional development course to support delivery of tiered school-based rehabilitation services
Bibliographic record
Abstract
Purpose The Facilitating Integration of Rehabilitation Services Through Training (FIRST) Course provides online professional development on tiered service delivery models for rehabilitation professionals working in education settings. Created by content and e-learning experts, this study describes our use of the Analysis, Design, Development, Implementation, Evaluation (ADDIE) instructional design model and the Successive Approximation Model (SAM) to develop, implement, and evaluate the FIRST Course, and reports the findings of an initial program evaluation.Method Rehabilitation professionals who completed the FIRST Course were invited to complete a cross-sectional survey to evaluate its utility.Results Between May 1, 2020, and August 11, 2023, 314 occupational therapists, 54 physiotherapists, and 170 speech-language pathologists completed the online course and survey. Respondents perceived the FIRST Course content to be relevant to their practice and to meet their learning needs regarding tiered services in education settings. Most respondents viewed the course positively and would recommend it to colleagues. More experienced respondents suggested a need for training on tiered service delivery models beyond an introductory level.Conclusions The ADDIE and SAM instructional design models were successfully applied to develop, implement, and evaluate online professional development for school-based rehabilitation professionals who wish to learn about tiered service delivery models.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".