Who is caring for nurses? A qualitative description of psychological influence of COVID-19 pandemic on RNs’ self-efficacy and job satisfaction
Bibliographic record
Abstract
Working while undertaking graduate education in nursing is challenging at any time. During the COVID-19 pandemic, many nurses continued to work on the frontline while completing their graduate studies. Healthcare workers, including nurses, were routinely exposed to several types of psychological trauma during the COVID-19 pandemic. In this study, we seek to generate an understanding of the psychological influence of COVID-19 on registered nurses’ (RNs’) self-efficacy and job satisfaction while commencing graduate studies in nursing and working in clinical practice during the pandemic. A qualitative descriptive design was used to explore written reflections from 72 RNs enrolled in their first Master of Nursing graduate course at an online university. The RNs’ online discussion postings related to the impact of the pandemic on nursing. Data were analysed using content and thematic analysis. Analysis revealed five overriding themes around job satisfaction and self-efficacy: level of professional involvement and guilt, communication of information and leadership, psychological and physical wellbeing, the safety of self and others, and relationships to and within the nursing profession. Overall, a strong sense of kinship contributed to job satisfaction and self-efficacy. Findings confirmed the need for so-called “aftercare” for nurses by leadership and administrators. The impact of the COVID-19 pandemic has been considerable on the individual nurse’s sense of self-efficacy and job satisfaction, and this is particularly noted in nurses who commenced graduate studies during the 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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".