Community and Hospital Healthcare Use by Adults With and Without Intellectual and Developmental Disabilities in Ontario, Canada, During the First 2 Years of the COVID‐19 Pandemic
Bibliographic record
Abstract
BACKGROUND: This study describes the proportion of Ontario adults with and without intellectual and developmental disabilities (IDD) who used community- and hospital-based healthcare in the first 2 years of the pandemic compared with the year pre-COVID-19. METHODS: Linked health administrative databases identified 87 341 adults with IDD and also adults without IDD living in Ontario, Canada. For each cohort, counts and proportions of adults who used different types of healthcare services were reported for the pre-COVID-19 year (16 March 2019 to 14 March 2020) and the first two COVID-19 years (15 March 2020 to 14 March 2021 and 15 March 2021 to 14 March 2022). RESULTS: Compared with the year prior to COVID-19, the proportion of adults with and without IDD who used health services was lower during the first COVID-19 year, but the likelihood of all types of visits increased during the second year. The likelihood of using homecare and of being hospitalized nearly returned to pre-pandemic levels. Virtual physician visits increased in each COVID-19 year from 5.2% prior to the pandemic to 13.0% in year 1 and 58.7% in year 2. For all years, the proportion of adults who used each service type was higher for those with IDD than without IDD. CONCLUSIONS: For adults with and without IDD in Ontario, Canada, during the first two COVID-19 years healthcare use decreased for all service types, except for virtual physician visits. In the second year, healthcare use increased but did not reach pre-COVID-19 levels. In all years, adults with IDD were more likely to use services than other adults.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| 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".