Children with Disabilities in Canada during the COVID-19 Pandemic: An Analysis of COVID-19 Policies through a Disability Rights Lens
Bibliographic record
Abstract
Children with disabilities were especially vulnerable during the COVID-19 pandemic, and policies designed to mitigate its effects were limited in addressing their needs. We analyzed Canadian policies related to children with disabilities and their families during the COVID-19 pandemic to identify the extent to which these policies aligned with the United Nations Convention on the Rights of Persons with Disabilities (UN CRPD) and responded to their mental health needs by conducting a systematic collection of Canadian provincial/territorial policies produced during the pandemic, building a categorization dictionary based on the UN CRPD, using text mining, and thematic analysis to identify policies' alignment with the UN CRPD and mental health supports. Mental health was addressed as a factor of importance in many policy documents, but specific interventions to promote or treat mental health were scarce. Most public health policies and recommendations are related to educational settings, demonstrating how public health for children with disabilities relies on education and community that may be out of the healthcare system and unavailable during extended periods of the pandemic. Policies often acknowledged the challenges faced by children with disabilities and their families but offered few mitigation strategies with limited considerations for human rights protection.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".