147 Analysis of coping strategies employed by nurses during the COVID-19 pandemic crisis by career paths
Bibliographic record
Abstract
Introduction Before the pandemic, nurses worldwide faced difficult working conditions, including staff shortages, heavy workloads, and traumatic experiences. 1 2 These factors contributed to overwork and chronic fatigue, negatively impacting both the quality of care provided and the health of caregivers.3 Studies conducted before COVID-19 revealed that nurses felt inadequately prepared and lacked the confidence to handle crises.4 The COVID-19 crisis intensified these issues, exposing structural and psychological vulnerabilities.Objectives This study aims to identify the coping strategies nurses employed during the COVID-19 health crisis, analyze their effects on professional trajectories, and propose mechanisms that healthcare institutions can utilize to support nurses’ adaptation in times of health emergencies.Methods As part of a mixed-methods study on nurses’ career paths, we conducted semi-structured interviews with 74 nurses categorized into five career trajectories. 5 6 Two research assistants conducted each interview (~1.5 hours). We employed journey mapping methodology and analyzed data using a framework approach, integrating inductive and deductive methods.7 8 Lazarus and Folkman’s Transactional Model of Stress and Coping guided our analysis.9–11 Results Sanitary measures shaped available coping strategies, underscoring the value of proactive approaches. Nurses who used problem-solving methods (planning, seeking support, adapting) managed stress more effectively than those relying on avoidance (consumption, denial, disengagement). High-risk area nurses reported elevated stress, while those who shifted roles fared better. Many underutilized employer-provided support resources. Some strategies facilitated crisis management but also introduced new challenges.Conclusion Understanding the coping strategies nurses use during the pandemic provides critical insights for strengthening resilience and improving retention. Healthcare institutions must adapt support mechanisms to foster long-term well-being and workforce stability.References Hegney DG, Craigie M, Hemsworth D, et al. Compassion satisfaction, compassion fatigue, anxiety, depression and stress in registered nurses in Australia: study 1 results. J Nurs Manag. May 2014;22(4):506–518.Khamisa N, Peltzer K, Oldenburg B. Burnout in relation to specific contributing factors and health outcomes among nurses: a systematic review. Int J Environ Res Public Health. May 31 2013;10(6):2214–2240.Alderson M, Parent-Rocheleau X, Mishara B. Critical review on suicide among nurses. Crisis. Jun 2015;36(2):91–101.Labrague LJ, Hammad K, Gloe DS, et al. Disaster preparedness among nurses: a systematic review of literature. Int Nurs Rev. Mar 2018;65(1):41–53.Fortin M-F, Gagnon J. Fondements et étapes du processus de recherche: méthodes quantitatives et qualitatives. Montréal: Chenelière éducation 2016.Gallagher F, Marceau M. La recherche descriptive interprétative. In: Corbière M, Larivière N, eds. Méthodes qualitatives, quantitatives et mixtes: dans la recherche en sciences humaines, sociales et de la santé. Vol 2e ed: Presses de l’Université du Québec; 2020:5–32.Davies EL, Bulto LN, Walsh A, et al. Reporting and conducting patient journey mapping research in healthcare: a scoping review. J Adv Nurs. Jan 2023;79(1):83–100.Miles MB, Huberman AM, Saldaña J. Qualitative data analysis : a methods sourcebook. Fourth edition ed. Los Angeles: SAGE; 2020.Lazarus R, Folkman S. Stress, Appraisal, and Coping. New York: Springer; 1984.Lazarus RS, Folkman S. Transactional theory and research on emotions and coping. European Journal of Personality 1987;1(3):141–169.Carver CS, Scheier MF, Weintraub JK. Assessing coping strategies: a theoretically based approach. J Pers Soc Psychol. Feb 1989;56(2):267–283.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".