Resilience amongst Ontario registered practical nurses in long‐term care homes during <scp>COVID</scp> ‐19: A grounded theory study
Bibliographic record
Abstract
AIMS: This study aimed to understand how the personal and professional resilience of Registered Practical Nurses working in long-term care (LTC) homes in Ontario were impacted during the Coronavirus 2019 pandemic. BACKGROUND: Registered Practical Nurses are primary regulated healthcare providers that have worked in Ontario LTC homes during the COVID-19 pandemic. As frontline workers, they have experienced increased stress secondary to lockdowns, changing Ministry of Health recommendations, social isolation and limited resources. LTC homes experienced almost a third of all COVID-19-related deaths in Ontario. Understanding registered practical nurses' (RPNs) resilience in this context is vital in developing the programs and supports necessary to help nurses become and stay resilient in LTC and across a range of settings. METHODS: Purposive sampling was used to recruit 40 Registered Practical Nurses working in LTC homes across Ontario for interviews. Charmaz's Grounded theory guided in-depth one-on-one interviews and analyses completed between April to September 2021. RESULTS: Registered Practical Nurse participants represented 15 (37.5%) private, and 25 (62.5%) public LTC homes across Ontario Local Health Integration Networks. Findings informed two distinct perspectives on resilience, one where nurses were able to maintain resilience and another where they were not. Sustaining and fraying resilience, presented as bimodal processes, was observed in four themes: 'Dynamic Role of the Nurse', 'Preserving Self', 'Banding Together' and 'Sense of Leadership Support'. CONCLUSION: Resilience was largely drawn from themselves as individuals. Resources to support self-care and work-life balance are needed. Additionally, workplace supports to build capacity for team-based care practices, collegial support in problem-solving and opportunities for 'connecting' with LTC nursing colleagues would be beneficial. Our findings suggest a role for professional development resources in the workplace that could help rebuild this workforce and support RPNs in providing quality care for older adults living in LTC. PATIENT OR PUBLIC CONTRIBUTION: Our research team included two members of the Registered Practical Nurses Association of Ontario, and these team members contributed to the discussion and design of the study methodology, recruitment, analysis and interpretation. Further, RPNs working in long-term care during the COVID-19 pandemic were the participants in this study and, therefore, contributed to the data. They did not contribute to data analysis or interpretation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".