Promoting Participation-Focused Practices in Inclusive Educational Settings: Stakeholders’ Perspectives Following a Knowledge Translation Initiative
Bibliographic record
Abstract
IMPORTANCE: Available participation-focused interventions (e.g., the Pathways and Resources for Engagement and Participation intervention [PREP]), are not well integrated into practice, indicating a knowledge-to-practice gap. OBJECTIVE: To explore the perspectives of school-based (SB) occupational therapists and occupational therapy managers following knowledge translation (KT) programs for facilitating participation-based practices in inclusive education. DESIGN: This was an implementation qualitative study. After taking part in KT programs for implementing PREP, which were tailored to the needs and contexts of each stakeholder group, occupational therapists participated in focus groups, and occupational therapy managers responded to open-ended survey questions. A content analysis was done separately for each group, then integrated and organized according to the Participation-focused KT (P-KT) framework. SETTING: Inclusive educational setting. PARTICIPANTS: Seven occupational therapists working in inclusive schools and 39 occupational therapy managers. RESULTS: We identified 10 themes addressing the micro, meso, and macro levels of the P-KT framework across the two groups. Five themes, common to all participants, addressed micro (e.g., setting participation-focused goals) and meso (e.g., school-PREP fit) levels. Macro-level themes were addressed by managers only (e.g., anchoring PREP to existing policy of the Israeli Ministry of Education). CONCLUSIONS AND RELEVANCE: Targeting different stakeholders at the micro and meso levels of the educational system can initiate a shift in practice toward a focus on participation. KT intersectoral initiatives at the macro level (e.g., targeting community services and local policies), and engaging other stakeholders (e.g., students, families, and educators), are required to ensure the sustainability of participation-based practices. What This Article Adds: The authors present two knowledge translation programs that target both occupational therapists and managers in the educational system. The programs promote a shift in school-based occupational therapy services toward a focus on participation and environment.
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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 | 0.064 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".