‘Participate to learn’: A promising practice for community ABI rehabilitation
Bibliographic record
Abstract
OBJECTIVE: To identify best practices and promising practices to enhance participation in meaningful and productive activities. METHOD: An electronic search of the ABI rehabilitation research literature since 1990 yielded 974 articles of which 30 focused on interventions that targeted participation and evaluated effectiveness using direct measures of participation. Three reviewers rated these articles according to the standards set out by the Centre for Reviews and Dissemination. Following the systematic review, an interpretive review of the same articles was completed. RESULTS: Only three studies were rated as strong. No best practices were identified. Three promising practices found some support. The interpretive review suggested 'Participate to learn' as a useful rehabilitation model. The model rests on roles as goals, learning by experience in real-life contexts and the use of personal and environmental support to enable participation. CONCLUSIONS: 'Participate to learn' is both a credible rehabilitation model and deserving of more study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.061 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| 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".