A longitudinal qualitative study of clinical nurses caring for hospitalized adults during the first fifteen months of COVID-19: lessons in professional survival and leadership
Bibliographic record
Abstract
BACKGROUND: The goal of this study was to record and analyze the initial and lingering thoughts and reactions that 41 clinical nurses experienced while caring for individuals hospitalized with COVID-19 during the initial April 2020 surge and over the next 15 months while vaccines and effective treatments became available. The sample represented a diverse group of nurses at one American health care institution in terms of gender identity, age, ethnic group, years of experience, clinical specialty area, and hospital shift worked. At the time of this April surge, nurses were exposed to a virus with no standard treatments and high mortality rates. Using a longitudinal qualitative design, the team hoped to answer questions about what compelled these nurses to continue to come to work? And what leadership lessons about identity, communication, camaraderie can be learned from this unplanned crisis? METHODS: A longitudinal design was chosen to capture the changes in nurses’ thoughts about themselves as professionals and their clinical work during COVID-19 after the World Health Organization (WHO) declared COVID-19 a global pandemic on March 11, 2020 (Cucinotta and Vanelli, Acta Biomed 91(1):157–60, 2020 [1]). The research team wondered how a large American suburban medical center staff was dealing with this virus and if there were changing patterns in the experiences and thoughts of clinical nurses as the virus evolved in 2020–2021. We applied the 18th Century philosophy of Immanuel Kant writings on moral thinking to examine the nurses’ thoughts of continuing their clinical work during this time. Using a classic Western philosopher seemed relevant given the worldwide nature of the pandemic which affected everyone, not just health care workers. Interview questions from the literature and research team experiences were developed; an additional file provides details of the interview schedule in English (See Additional file 1 for the interview schedule). Forty-one RNs agreed to participate; each nurse agreed to three interviews over three phases. The first phase occurred from April through November 2020 with 41 RNs participating. Thirty-five RNs from the original 41 returned for a second interview between November 2020 through April 2021. In the final phase the same 35 RNs took part in a final interview between April 2021 and July 2021. We asked the same questions to all nurses during each phase. Similar quotations were grouped into 42 codes, then like codes were collected into five themes: Exposure risk, Professional Self-Image, Communication, Community Reaction and Finding an Emotional Balance. These themes provided a context for describing the ethos of nurses working with pandemic patients. RESULTS: These nurses put the needs of the greater community before their personal desires. They came to work every day because they believed that they had essential knowledge and skills to help in the pandemic. Despite the realization that many of their patients would die, the nurses adapted their skills within strict isolation protocols. Their greatest stress was infecting their families, a misfortune that did not happen. Effective leadership and the camaraderie that developed among the nurses and other staff sustained them during this time. Interview quotations seemed to mirror interviews conducted by one team researcher on military nurses who served in Vietnam (1962–1975). Comparing these citations suggests that the personal danger, inability to save people and an overwhelming number of patients that taxed resources were similar to veteran and civilian nurses. CONCLUSION: The 35 clinical nurses in this study worked through the initial fifteen months of COVID-19 pandemic in America. Kant’s idea of duty for the greater good over personal needs provided an explanation and rationale why the 35 clinical nurses remained in clinical work. Reviewing the transcripts indicated that there seemed to be parallels between nurses’ clinical experiences in the Vietnam War and the COVID-19. Their experience indicated that planning for future disasters is imprecise. Knowledge gleaned from this study suggests that a combination of external factors and personal assets should provide the support for nurses and other health care workers in future plagues. TRIAL REGISTRATION: This study was registered retrospectively on ClinicalTrials.gov NCT06012539 on August 23, 2023.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".