An umbrella review of the characteristics of resiliency-enhancing interventions for children and youth with disabilities
Bibliographic record
Abstract
PURPOSE: The current umbrella review aimed to identify key intervention characteristics that have been demonstrated to enhance resiliency in children and youth with disabilities. MATERIALS AND METHODS: To identify these key ingredients, using JBI guidelines, we conducted comprehensive searches in the fall of 2022 and searches were re-run in June 2023. Using the PICO format, we searched for peer-reviewed review articles that included children and youth with disabilities (6 to 19 years of age), the intervention targeted resiliency, the context was home, school, or community, and the outcome was resiliency enhancement. RESULTS: The initial searches produced 1031 articles, of which 4 met our inclusion criteria. These articles collectively had reviewed a total of 247 articles representing approximately 2756 participants. We found a wide range of disabilities represented in the studies and many activities that are included in the existing REIs. Our findings identified engagement, self-regulatory processes, capacity building, positive social connectedness, and a customized intervention approach as evidence-based resiliency enhancing features. We propose a model consolidating these findings into a multi-dimensional resiliency process which may help explain successful adaptation. CONCLUSION: Our proposed model may be helpful in delineating entry points that different REIs have used to generate positive change.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.036 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".