A Preliminary Study to Develop a Collaborative Tiered School-Based Physical Therapy Service Delivery Model: Results from an International Delphi Consultation
Bibliographic record
Abstract
Background: Physical therapy (PT) is increasingly provided at schools to help students participate in educationalactivities. Recent rehabilitation models emphasized the benefits of using collaborative tiered services for serviceprovision, yet no model is available to guide how these services should be delivered. Therefore, this study aims to determine the core attributes and PT interventions of a collaborative tiered school-based PT model that could guide how PT services are delivered in schools worldwide.Methods: A modified Delphi method was used to identify the core attributes and the PT interventions that would be part of the model. An introductory webinar followed by three Delphi rounds with 24 international experts was conducted. Similar ideas generated in Round 1 were combined into statements; the statements reaching the predetermined consensus level in Rounds 2 or 3 were retained. Categories were created to present core attributes and Tiered interventions that were retained.Results: 41 core attributes were identified and grouped under seven categories. Tiered interventions were grouped under 15 categories which included 37 interventions for Tier 1, 24 interventions for Tier 2, and 60 interventions for Tier 3.Conclusion: The recommended core attributes and interventions will support the development of an international framework for school-based PT services, fostering health promotion for all children, and supporting those with disabilities.
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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 | 0.080 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".