Barriers Experienced by Children with Disabilities and Caregivers During the First Wave of COVID-19: A Social-Ecological Perspective
Bibliographic record
Abstract
This paper examines the results of a Pan-Canadian online survey of caregivers of children with disabilities (n=247) that explored the barriers and unmet needs experienced by children with disabilities and their caregivers during the first wave of school closures during the COVID-19 pandemic. A purposive sample of caregivers of children with disabilities between the ages of 4-21 years were recruited through disability advocacy organisations. A thematic analysis was conducted of caregivers’ responses to 3 open-ended survey questions not previously examined. The themes were analysed using Bronfenbrenner’s ecological systems framework and revealed attitudinal, structural, and policy barriers to equity and inclusion for children with disabilities and their caregivers at multiple levels of the social ecology. The themes included the microsystem (Multiple and complex roles for caregivers), Mesosystem (Is anyone out there?), Macrosystem (Disability exclusive decisions), Exosystem (Left behind), and Chronosystem (Stress and giving up). Recommendations for policy and practice include strengthening teacher training in inclusive pedagogies, enacting participatory policy co-design that prioritises the needs of children with disabilities and caregivers, increasing regulation of individualised education planning, and providing supports that address the well-being of children with disabilities and caregivers.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".