The Medical Complexity of Newly Admitted Long-Term Care Residents Before and During the COVID-19 Pandemic in Ontario, British Columbia, and Alberta: A Serial Cross-Sectional Study
Bibliographic record
Abstract
The COVID-19 pandemic had profound effects on the long-term care (LTC) setting worldwide, including changes in admission practices. We aimed to describe the characteristics and medical complexity of newly admitted LTC residents before (March 1, 2019 to February 29, 2020) and during (March 1, 2020 to March 31, 2021) the COVID-19 pandemic via a population-based serial cross-sectional study in Ontario, Alberta, and British Columbia, Canada. With data from the Minimum Data Set 2.0 we characterize the medical complexity of newly admitted LTC residents via the Geriatric 5Ms framework (mind, mobility, medication, multicomplexity, matters most) through descriptive statistics (counts, percentages), stratified by pandemic wave, month, and province. We included 45 756 residents admitted in the year prior to and 35 744 during the first year of the pandemic. We found an increased proportion of residents with depression, requiring extensive assistance with activities of daily living, hip fractures, antipsychotic use, expected to live <6 months, with pneumonia, low social engagement, and admitted from acute care. Our study confirms an increase in medical complexity of residents admitted to LTC during the pandemic and can be used to plan services and interventions and as a baseline for continued monitoring in changes in population characteristics over time.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".