Partnering to explore the needs and priorities of stakeholders involved in a community‐based dance program for children with disabilities
Bibliographic record
Abstract
Results: Thirty-seven articles met the inclusion/exclusion criteria.Most studies (n=29) described social inclusion of students with disabilities and used a cross-sectional questionnaire with these students and their peers.Other studies examined service delivery (n=4), knowledge mapping (n=1), comorbidity analysis (n=1) and family networks (n=1), largely using cross-sectional designs.Only three intervention studies were identified.There is a scarcity of research exploring global, or system networks, as no study pertained to the influence of networks on the dissemination of innovation or on the evaluation of program implementation at the organizational or system levels.The most frequently reported network properties were reciprocity (the extent to which two actors have nominated each other); degree centrality (the number of ties directly related to an actor/institution) and network density (proportion of potential ties that are actual ties between all points of the network).Conclusions/Significance: To our knowledge, this is the first study reviewing the use of SNA in childhood disability research.This study informs of the opportunity to expand our knowledge base outside of the school setting to include system-levels networks, such as healthcare professionals and disability advocacy networks.These findings provide a foundation for researchers and professionals working in childhood disability to inform future research and considerations to support social inclusion from a network perspective, and to transform the way we spread evidence-based information and promote health and well-being for children with disabilities and their families.
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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".