Unveiling the impact: understanding long-term care workers’ experiences and their perceptions of resident challenges amidst the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, long-term care (LTC) facilities in Canada were confronted with many rapidly changing public health safety guidelines. Based on the guidelines, LTC facilities had to implement a series of virus containment and mitigation measures, presenting significant challenges for both workers and residents. This research aims to provide insights that could be used to guide improvements in the experiences of LTC workers, and of residents, in future pandemic crises. METHODS: A qualitative multi-case study was used to explore the pandemic experiences of a demographically diverse group of LTC workers in Canada, focusing on how public health safety guidelines impacted them, and their perceptions of challenges faced by residents. Fourteen workers were engaged from facilities in Nova Scotia and British Columbia, which are regions distinct geographically and with differences in safety guidelines and implementation. Semi-structured interviews were conducted between April to October 2021. Using thematic analysis, we identified patterns within and across the interview transcripts. RESULTS: The thematic analysis provided an understanding of the experiences and perspectives of LTC workers. There were four key themes: (1) Tangling with Uncertainty, that describes the effects of ambiguous messaging and shifting COVID-19 safety guidance on workers; (2) Finding Voice, that highlights how workers coped with feelings of helplessness during the healthcare crisis; (3) Ripple Effects, of pandemic pressures on workers beyond resident care, that included strengthening of inter-colleague support as well as financial challenges, and; (4) Loss of Home, where workers perceived that protection of residents led to a loss of the residents' home environment, personal freedom, and autonomy. CONCLUSIONS: The findings suggest that LTC workers' experiences during future pandemics may be improved by their inclusion in the development of public health safety guidelines, facilitating inter-colleague support systems, and ensuring worker financial stability. A balance should be found between preventing infection in LTC facilities and retaining the principles of holistic and resident-centered care for workers' and residents' mental health benefits.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".