Lessons from Long-Term Care Facilities without COVID-19 Outbreaks
Bibliographic record
Abstract
BACKGROUND: The COVID-19 crisis in long-term care (LTC) homes was devastating for residents and front-line workers. Recent reports have detailed what went wrong in LTC facilities, including equipment shortages, lack of preparedness, underestimation of COVID-19's virulence and bans on caregiver visits. Less is known about what went well in some facilities. PURPOSE: To describe nurses' and other staff members' experiences and lessons learned in two LTC facilities in Quebec that reported no COVID-19 outbreaks during the first wave of the pandemic. METHODS: Methods: A case study design guided by appreciative inquiry was conducted, in which a case was defined as a LTC facility without COVID-19 outbreaks; two cases were included. Twenty-three healthcare team members from the two sites were recruited and interviewed between October and November, 2020. RESULTS: Several common themes were identified: being informed and respecting outbreak protocols; the presence of key outbreak protocols, which allowed for stable teams; a clear action plan; and access to materials and resources. Key management themes included team support and reward, ongoing communication and providing compassionate care to residents. CONCLUSION: This study highlights several lessons learned that have the potential to strengthen the LTC health system.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".