The Canadian Long-Term Care Sector Collapse from COVID-19: Innovations to Support People in the Workforce
Bibliographic record
Abstract
The COVID-19 pandemic rattled Canada's long-term care (LTC) sector by exacerbating the ingrained systemic and structural issues, resulting in tragic consequences for the residents, family members and LTC staff.At the core of LTC's challenges is chronic under-staffing, leading to lower quality of care for residents and higher degrees of moral distress among staff.A rejuvenation of the LTC sector to support its workforce is overdue.A group of diverse and renowned researchers from across Canada set out to implement innovative evidenceinformed solutions in various LTC homes.Their findings call for immediate action from policy makers and LTC decision makers. PROMISING PRACTICE INTERVENTIONS* Co-lead authors. Key Takeaways• On top of the long-standing lack of resources, including the ever-existing staff shortages, long-term care (LTC) workers experienced an unprecedented increase in their workload during the pandemic without justifiable compensation -including a lack of sufficient time off and absence of communication to promote work-life balance.• All categories of LTC staff experienced immense moral distress, burnout and compassion fatigue due to high rates of COVID-19 cases and deaths of residents and staff, policy changes and staff turnover.A preventative approach where staffing is optimized and supports are readily available is necessary to prepare the LTC sector for another health crisis.• Decades of research have shown that strong evidence is not sufficient to change LTC policies and practices.A rejuvenation of the LTC sector urgently requires adequate staffing with access to competitive benefits, compensation and mental health supports to allow workers to effectively care for residents and effectively implement changes to improve LTC in Canada.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".