The Impact of the COVID-19 Pandemic on Homecare Use for Individuals with Physical Disabilities Stratified by Sex, Age, and Mental Health Condition: A Cohort Study Using Administrative Health Data
Bibliographic record
Abstract
PURPOSE: This study investigated the impact of the COVID-19 pandemic on homecare service use among individuals with physical disabilities, stratified by age, sex, and mental health conditions. METHODS: Monthly utilization of personal support and nursing services was assessed using linked health administrative databases from ICES in Ontario, Canada, over two periods: pre-pandemic (March 2015 to February 2020) and during the pandemic (March 2020 to June 2022). Predictive Autoregressive Integrated Moving Average (ARIMA) models were used to estimate changes in service use. RESULTS: During the pandemic, personal support service use declined significantly across multiple subgroups with some groups experiencing greater impacts. Significant decreases were observed in 78.5% of months for males, 14.3% for females, 78.5% for individuals aged 65 years and younger, 17.9% for those older than 65 years, and 78.5% for individuals with mental health conditions. In contrast, nursing service use increased significantly, with significant increases observed in 85.7% of months for males, 60.7% for females, 60.7% for those aged 65 years and younger, 17.9% for those older than 65 years, 85.7% for individuals with mental health conditions, and 28.6% for those without mental health conditions. CONCLUSION: The findings highlight substantial variation in the pandemic's impact across subpopulations, with certain groups disproportionately affected. Targeted strategies are needed to mitigate these disparities and ensure equitable access to homecare services. Further research is warranted to explore the long-term implications and the underlying factors contributing to these differences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".