Tiered Approaches to Rehabilitation Services in Education Settings: Towards Developing an Explanatory Programme Theory
Bibliographic record
Abstract
Rehabilitation services in education settings are evolving from pull-out interventions focused on remediation for children and youth with special education needs to inclusive whole-school tiered approaches focused on participation. A limited number of discipline-specific practice models for tiered services currently exist. However, there is a paucity of explanatory theory. This realist synthesis was conducted as a first step towards developing a middle-range explanatory theory of tiered rehabilitation services in education settings. The guiding research question was: What are the outcomes of successful tiered approaches to rehabilitation services for children and youth in education settings, in what circumstances do these services best occur, and how and why? An expert panel identified assumptions regarding tiered services. Relevant literature (n = 52) was located through a systematic literature review and was analysed in three stages. Several important contextual characteristics create optimal environments for implementing tiered approaches to rehabilitation services via three main mechanisms: (a) collaborative relationships, (b) authentic service delivery, and (c) reciprocal capacity building. Positive outcomes were noted at student, parent, professional, and systems levels. This first-known realist synthesis regarding tiered approaches to rehabilitation services in education settings advances understanding of the contexts and mechanisms that support successful outcomes.
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".