Towards multi-faceted outcomes of participation-based interventions: mapping the PREP’s effects for children and youth with disabilities
Bibliographic record
Abstract
PURPOSE: Systematically organizing participation effects may guide participation-based research designs in rehabilitation. This perspective paper uses existing evidence on Pathways and Resources for Engagement and Participation (PREP) to showcase the multitude of effects from a single intervention and synthesize the magnitude of these effects. METHODS: An outcome matrix of participation effects comprising three dimensions (intermediate, instrumental, ultimate) and two levels (transient, enduring) was used to systematically map PREP's effects. Forest plot demonstrated clinically important changes in the Canadian Occupational Performance Measure (COPM) across studies. Effect sizes were calculated. RESULTS: The majority of outcomes from 11 studies were mapped to ultimate-transient effects (e.g., changes in participation of self-chosen activities), followed by instrumental-transient effects (e.g., changes in motor body functions). Fewer outcomes were mapped to ultimate-enduring effects (e.g., changes of participation for a longer period or across settings) or intermediate-enduring effects (e.g., therapist-applied knowledge), demonstrating the gaps for investigating enduring effects. COPM changes in most studies (89%) showed clinical significance with small to large effects. CONCLUSIONS: Systematic mapping from PREP example guides categorizing multidimensional outcomes. Future participation-based studies can employ individual-based mixed-methods designs to delve into the long-lasting enduring outcomes of youth capacity-building and the transformative process of pursuing meaningful participation goals.
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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 | 0.055 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".