Caring for residents with dementia during a COVID‐19 outbreak in a long‐term care home
Bibliographic record
Abstract
BACKGROUND: Although there have been considerable public concerns about the impact of COVID-19 on residents living in long-term care homes, much less attention has focused on lessons learned from staff experiences about caring for people with dementia during outbreaks. The outbreaks added significant additional stress to the nursing workforce, which has historically experienced high turnover, chronic staffing shortages, and increased burnout in long-term care settings. We conducted focus groups (n=20) and individual interviews (n=10) to investigate critical challenges, experiences, and support needed for frontline staff in a long-term care home in British Columbia, Canada. A total of 30 staff in multiple disciplines participated in the study. They included Registered Nurses, Licenced Practical Nurses, care staff, recreational staff, and unit clerks. We applied qualitative thematic analysis and identified four themes: (a) I am proud, (b) we become stronger, (c) I am nervous (d) the vaccine helps. The frontline staff's voices provided a detailed description of their emotional experiences, creative coping strategies and positive stories about caring for the most vulnerable population in extraordinary situations. In our poster, lessons learned and implications for future research and practice will be explored and discussed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".