Experiences of Care Providers Working in Long-Term Care during the COVID-19 Pandemic
Bibliographic record
Abstract
INTRODUCTION: In this qualitative study, care providers from long-term care homes were interviewed to explore how they experienced, coped with, and adapted to care shifts during the COVID-19 pandemic. METHODS: Thirteen multidisciplinary care providers and 24 supervisory and administrative staff participated in either a focus group or individual interview between July 2021 and February 2022. Participants were front-line care providers in 5 urban long-term care homes in western Canada. RESULTS: Care providers described negative impacts on residents and family members related to service delivery, restricted visiting, and quarantining protocols. They also identified negative impacts they experienced as care providers including fear and uncertainty, exhaustion, concerns about care provision, lower morale, and job self-efficacy. Buffers to stress comprised working as an integrated team and organizational support. Opportunities for growth and development and being adaptive were also described. Recommendations focused on organizational pandemic readiness and the importance of holistic care. CONCLUSION: These findings highlight the need to proactively ensure a supportive infrastructure, wellness-promoting work culture, and a sustainable resource plan to help care providers pivot and adapt in a pandemic.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".