Socio-demographic disparities in receipt of clinical health care services during the COVID-19 pandemic for Canadian children with disability
Bibliographic record
Abstract
BACKGROUND: Little is known about the experience of receiving in-person and virtual clinical health care services during the COVID-19 pandemic for Canadian children with developmental disabilities and delays facing multiple layers of vulnerability (e.g., low income, low educational attainment families). We examined the relationship between socio-demographic factors and the receipt of these services (physical and mental health services) during COVID-19 for Canadian children with these conditions. METHODS: Data collected in Canada for the Global Report on Developmental Delays, Disorders and Disabilities were used. The survey: (1) was developed and disseminated in collaboration with caregivers of children with disabilities, (2) included topics such as response to the pandemic and receipt of services and supports, and (3) documented the experiences of a non-random convenience sample of caregivers of children (any age) with these conditions during and prior to the pandemic. We used four logistic regression models to assess the association between socio-demographic factors and receipt of services. RESULTS: Being a single parent, having low educational attainment (high school or less), having low income (making less than $40,000 per year), working less than full time (working part-time, working reduced hours due to COVID, retired, stay home parent or student), as well as male gender and older age of the child with disability were factors associated with decreased likelihood of receiving services. CONCLUSION: Our findings point to the need for tailoring services for families of children with disabilities, particularly low socioeconomic status families, to ensure continuity of care during public health emergencies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".