Exploration of the occupational and personal dimensions impacted by the <scp>COVID</scp> ‐19 pandemic for nurses: A qualitative analysis of survey responses
Bibliographic record
Abstract
AIM: To explore the occupational and personal life dimensions that have been impacted by the COVID-19 pandemic for registered nurses (RN) and licensed practical nurses (LPN). DESIGN: Qualitative interpretive description approach. METHODS: Between July and September 2020, a web-based cross-sectional study was conducted among RNs and LPNs in Quebec, Canada. Included in this survey was an open-ended question allowing nurses to describe the occupational and personal life dimensions that were impacted by the COVID-19 pandemic. Thematic analysis was used to interpret the qualitative data from this open-ended question. Reporting followed the Standards For Reporting Qualitative Research (SRQR). RESULTS: Of the 1860 survey respondents, 774 RNs and 43 LPNs responded to the open-ended question (total n = 819). For the occupational dimension, six themes were identified: impacts of infection control on work, change in daily work tasks, offloading and reorganization of care, deterioration of working conditions, increased stress at work and issues related to the profession. For the personal dimension, four themes were found: impacts on the family, dealing with changes about leisure and personal life, impacts on physical and psychological health. CONCLUSION: Knowing the dimensions affected by the COVID-19 pandemic could help to identifying appropriate interventions to support RNs and LPNs. IMPACT: The COVID-19 pandemic has significantly impacted the occupational and personal lives RNs and LPNs working in the Quebec healthcare system. More specifically, Quebec's nurses experienced a major reorganization of care generated by important government decisions. Knowing how the pandemic affected different life dimensions will help in the development of support adapted to nurses' realities. Even in a pandemic context, improvements in the work environment or appropriate support could lead to an improved psychological health for nurses.
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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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".