A Leave of Absence Might Not Be a Bad Thing: Registered Practical Nurses Working in Home Care During the COVID-19 Pandemic
Bibliographic record
Abstract
To describe the resilience and emotional intelligence of Registered Practical Nurses working in Home and Community Care during the COVID-19 pandemic. Specifically, to determine if there was a relationship between resilience and emotional intelligence based on whether a nurse: (1) left the sector, (2) considered leaving, or (3) took a leave of absence during the pandemic. An online cross-sectional survey was used to capture respondents’ demographic information and scores on the Connor–Davidson Resilience Scale, Resilience at Work Scale ® , and Wong and Law Emotional Intelligence Scale. Registered Practical Nurses working, or who had worked, in Home and Community Care January 2020 to September 2022 were eligible to participate. The Checklist for Reporting Results of Internet E-Surveys was used. The survey was available June to September 2022 and advertised by the Registered Practical Nurses Association of Ontario to approximately 2105 members. Descriptive statistics and independent samples t-tests were used to analyze results at a level of P < .05 was used for all analyses. A total of 672 respondents participated (completion rate = 92.8%). There were no differences on resilience or emotional intelligence scores based on whether a nurse left, or considered leaving, the Home and Community Care sector during the pandemic. However, nurses who took a leave of absence scored significantly higher on resilience and emotional intelligence measures when compared to those who did not. Results suggest that a leave of absence for these nurses during the pandemic may have been a supportive coping strategy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".