Occupational stressors and coping mechanisms among obstetrical nursing staff during the COVID-19 pandemic: a qualitative study
Bibliographic record
Abstract
Abstract Background Due to heightened occupational stress throughout the COVID-19 pandemic, hospital nurses have experienced high rates of depression, anxiety, and burnout. Nurses in obstetrical departments faced unique challenges, such as the management of COVID-19 infection in pregnancy with limited evidence-based protocols and the unknown risks of the virus on pregnancy and fetal development. Despite evidence that obstetrical nurses have experienced high levels of job stress and a decrease in job satisfaction during the COVID-19 pandemic, there is less known about the working conditions resulting in these changes. Using the Job Demands-Resources (JD-R) model, this study aims to offer insight into the COVID-19 working environment of obstetrical nurses and shed light on their COVID-19 working experiences. Methods The study was conducted using a qualitative approach, with data collection occurring through semi-structured interviews from December 2021 to June 2022. A total of 20 obstetrical nurses recruited from the obstetrical departments of a tertiary hospital located in Ontario, Canada, participated in the study. Interviews were audio-recorded, transcribed verbatim, and coded using NVivo. Data was analyzed using a theoretical thematic approach based on the JD-R model. Results Four themes were identified: (1) Job stressors, (2) Consequences of working during COVID-19, (3) Personal resources, and (4) Constructive feedback surrounding job resources. The findings show that obstetrical nurses faced several unique job stressors during the COVID-19 pandemic but were often left feeling inadequately supported and undervalued by hospital upper management. However, participants offered several suggestions on how they believe support could have been improved and shared insight on resources they personally used to cope with job stress during the pandemic. A model was created to demonstrate the clear linkage between the four main themes. Conclusions This qualitative study can help inform hospital management and public policy on how to better support and meet the needs of nurses working in obstetrical care during pandemics. Moreover, applying the JD-R model offers both a novel and comprehensive look at how the COVID-19 hospital work environment has influenced obstetrical nurses' well-being and performance.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".