Capacity building for youth with disabilities: principles and key ingredients identified through a scoping review
Bibliographic record
Abstract
PURPOSE: This knowledge synthesis aimed to 1) Map the extent and nature of the literature on capacity building in the field of rehabilitation for transition-age youth with disabilities (12-30 years old) and 2) Describe how capacity building is conceptualized and identify principles and key ingredients underpinning this concept. MATERIALS AND METHODS: A scoping review using JBI methodology was employed. A search of six databases resulted in 2169 English documents; 34 were retained. Two reviewers charted and analyzed the data, supported by the third reviewer. Inductive content analysis was used to identify principles and key ingredients. RESULTS: Seven documents provided explicit definitions of capacity or capacity building. Content analysis revealed four principles describing capacity building as: 1) individualized approach with real-world application 2) fostering a preferred future 3) youth taking ownership for change and 4) an ongoing process. Six key ingredients detail how to build capacity: 1) individualized and flexible approach in natural context 2) shared responsibility 3) use of accessible information and resources 4) cultivate strengths 5) opportunities for full participation and 6) facilitate reflection on experiences. CONCLUSION: Clinicians and researchers can draw upon identified capacity building principles and ingredients to support meaningful real-world outcomes for transition-age youth.
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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 | 0.052 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.026 | 0.023 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 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".