Using Technology to Enhance Services and Supports for Children and Youth with Disabilities and Medical Complexity and their Families in Canada: A Scoping Review
Bibliographic record
Abstract
In Canada and internationally, the use of technology to enhance community-based health and developmental services and supports with children and youth has increased dramatically as a result of the COVID-19 pandemic. In the Canadian context, how technology-enabled supports and services have developed and can be integrated as a long-term option in addition to in-person services requires further examination. This scoping review maps out existing and emerging themes in Canadian research published on how different technology modalities are being used in home and community settings with children and youth with disabilities and medical complexity and their families. A literature search conducted across seven databases between 2011 and 2023 resulted in potentially relevant publications, of which 12 met the inclusion criteria. The findings provide insights into how various technologies are being used and combined in order to provide parenting training and coaching and timely access to assessments in a continuum of care without the burden of travel time and costs. Also, the potential of technology to create peer support networks both for parents and older youth with disabilities. These findings can inform funding bodies and community organizations serving this population in undertaking in depth and critical analyses of how technology can be further integrated into hybrid models of service delivery. Further research and actions are also needed to address issues of digital equity.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.028 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".