COVID-19 and the Experiences and Needs of Staff and Management Working at the Front Lines of Long-Term Care in Central Canada
Bibliographic record
Abstract
Across the globe, long-term care has been under increased pressure throughout the COVID-19 pandemic. This is the first study to examine the experiences and needs of long-term care staff and management during COVID-19, in the Canadian context. Our group conducted online survey research with 70 staff and management working at public long-term care facilities in central Canada, using validated quantitative measures to examine perceived stress and caregiver burden; and open-ended items to explore stressors, ways of coping, and barriers to accessing mental health supports. Findings indicate moderate levels of stress and caregiver burden, and highlight the significant stressors associated with working in long-term care during the COVID-19 pandemic (i.e., rapid changes in pandemic guidelines, increased workload, "meeting the needs of residents and families", fear of contracting COVID-19 and COVID-19 coming into long-term care facilities, and concern over a negative public view of long-term care staff and facilities). A small subset (13.2%) of our sample identified accessing mental health supports to cope with work-related stress, with most participants identifying barriers to seeking help. Novel findings of this research highlight the significant and unmet needs of this high-risk segment of the population.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".