Staffing levels and expenses in Canadian long-term care facilities by ownership status before and during the COVID-19 pandemic
Bibliographic record
Abstract
Background: Low staffing levels and high turnover rates are longstanding issues in long-term care (LTC) facilities that were further exacerbated by the COVID-19 pandemic. Consequently, residents and staff were disproportionately affected, with high morbidity and mortality rates. This study examines changes in staffing levels, overall and by direct care worker category, across the LTC facilities sector by ownership status in Canada before and during the pandemic. It also explores differences in facility expenditures allocated towards employee wages, benefits, and subcontracts across homes by ownership status. Data and methods: Data were from the 2020 and 2021 Nursing and Residential Care Facility Survey, which collected information on facility characteristics, including expenses, revenue, ownership status, and staffing levels. Summary statistics and multivariate linear regression models were used to examine the association between staffing levels and ownership status, with analyses stratified by direct care worker category. Results: On average, public LTC facilities had higher staffing levels and spent a greater proportion of their total costs on employee wages and benefits before and during the pandemic, compared with for-profit and non-profit private facilities. While the total hours of care per resident day (HPRD) increased during the pandemic, there were notable variations by region, ownership status, and direct care worker category. For example, Ontario public nursing homes provided 10% more HPRD from registered nurses during the pandemic, compared with the period before. Interpretation: Staffing levels of direct care workers in LTC facilities, overall and separately, are associated with ownership status. Allocation of employee-related expenses also differed by ownership. Further research is needed to explore interactions between ownership status, staffing levels, and quality of care for residents.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".