Staff Experience of a Canadian Long-Term Care Home During a COVID-19 Outbreak: A Qualitative Study
Bibliographic record
Abstract
Abstract Background: COVID-19 has significant impact on long-term care (LTC) residents and staff. The purpose of this paper is to report the data gathered during a COVID-19 outbreak in a Canadian LTC home regarding staff experiences, challenges, and needs, to offer lessons learned and implications. Methods: A total of 30 staff from multiple disciplines participated in the study, including nurses, care workers, recreational staff, and a unit clerk. Focus groups (n=20) and one-on-one interviews (n=10) were conducted as part of a larger participatory action research (PAR) in a Canadian LTC home. All data collection was conducted virtually via Zoom, and thematic analysis was performed to identify themes.Results: Four main themes were identified: We are Proud, We Felt Anxious, We Grew Closer to Residents and Staff Members, and The Vaccines Help.Conclusions: This research details the resilience that characterizes staff in LTC, while highlighting the emotional toll of the pandemic, particularly during an outbreak. LTC staff in this study found innovative ways to connect and support residents and this resulted in stronger connections and relationships. Leadership and organizational support are pivotal for supporting team resilience to manage crisis and adapt positively in times of COVID-19 pandemic, especially during the period of outbreak.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".