Entering the Nursing Workforce during a Pandemic: An Interpretive Phenomenological Analysis (IPA) Study
Bibliographic record
Abstract
Background: In March of 2020, COVID-19 was declared to be a worldwide pandemic (WHO, 2022a). Since the pandemic was declared, various resources worldwide have been exhausted to accommodate the needs of the public; nurses have been significantly impacted by COVID. Although there is an overwhelming need for nurses to support the healthcare sector, there remains a critical worldwide shortage of nurses, including Registered Nurses (RNs), Registered Practical Nurses (RPNs) and Licensed Practical Nurses (LPNs). In the next decade, there will be a vacancy of 13 million nurses worldwide (Buchan et al., 2022). Novice nurses are graduating into a global pandemic that has exacerbated existing nursing shortage problems, unsafe patient ratios, increasing nursing vacancies, and severe burnout. Little is known about the lived experience of novice nurses working in this pandemic context. Research Question: What is the experience of novice nurses working during the COVID-19 pandemic in Ontario? Methods: A qualitative study was conducted, using Interpretive Phenomenological Analysis. Sample: 6 registered nurses were recruited who had graduated from a BScN program from 2019 to the present. The mean age was 25 years and the average number of years practicing nursing was 2.92 years. Semi-structured interviews were conducted, and data were analyzed. The overarching theme, COVID-19 as a catalyst was generated. Within this theme, three subthemes emerged: burnout, moral injury, and lack of nursing support. Conclusions: Burnout and lack of nursing support are concepts that have corroborated and added to pre-existing literature. Knowledge gleaned from this study has brought attention to moral injury and its negative psychological impact on novice nurses. Future research should focus on incidence and prevalence of moral injury in nurses in all areas of practice and education. Further, education and support of nurses should consider the development of coping skills that incorporate the tools of moral courage and moral resilience for all levels of nursing leadership.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".