Subcontracting, Employment Characteristics, and COVID-19 Infections Among Staff and Residents of Nursing Homes in Canada
Bibliographic record
Abstract
OBJECTIVES: Staffing challenges in nursing homes (NHs) have led to an increased reliance on subcontracted direct care workers from third-party agencies to provide essential care for older adults. This study assesses the associations between subcontracting, employment characteristics, and COVID-19 infections among direct care staff and residents in NHs. DESIGN: Retrospective observational study using 2 cycles of cross-sectional data from the Nursing and Residential Care Facilities Survey, administered across provinces in Canada during the pandemic, in 2020 and 2021. SETTING AND PARTICIPANTS: NHs (n = 823) that responded to both Nursing and Residential Care Facilities Survey cycles. METHODS: The mean number of COVID-19 cases, prevalence rates, and average number of subcontracted direct care workers (registered nurses, registered practical nurses, and personal support workers) were calculated per NH. Associations between subcontracting status, employment characteristics, and COVID-19 infections among NH direct care staff and residents were examined using multivariate negative binomial regression analyses. Employment characteristics included NH ownership type, size, hours of care per resident day, and working conditions, including experiences of staff shortages. RESULTS: Approximately 30% of NHs subcontracted direct care workers from agencies, contributing to 14% of total annual hours of direct care work. On average, the prevalence of staff and resident COVID-19 infections significantly varied by subcontracting status. NHs that subcontracted workers had 1.6 and 1.9 times greater rates of COVID-19 infections among staff and residents, respectively, after controlling for covariates. CONCLUSIONS AND IMPLICATIONS: This study found higher rates of COVID-19 infection in staff and residents of NHs that subcontracted direct care workers compared with those that did not. Increased direct care worker absenteeism, for-profit status, and size were also predictors of staff and resident infections. Future research is needed to identify and assess procedures for subcontracting agency workers that limit infection and improve quality of care.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".