Occupational Stressors and Coping Mechanisms Among Obstetrical Nursing Staff Throughout the COVID-19 Pandemic
Bibliographic record
Abstract
Background: As a result of heightened occupational stress throughout the COVID-19 pandemic,nurses in hospitals are experiencing high rates of depression, anxiety, and burnout. However,nurses in obstetrical departments have had unique challenges and have experienced specificsources of stress that remain unclear.Methods: Semi-structured interviews were conducted with twenty obstetrical nurses that workedat an Ontario tertiary care centre during the COVID-19 pandemic. Participants shared theirexperiences of working during the pandemic, focusing on job stressors, personal resources, anddesires for job resources. Interviews were audio-recorded, transcribed verbatim and coded usingNVivo. Data was analyzed using a theoretical thematic approach based on the Job Demands-Resources (JD-R) model.Results: Key job stressors identified included having an increased workload, fear of COVID-19transmission, providing proper patient care, and overwhelming physical demands. Moreover,participants expressed they felt undervalued, inadequately supported, and burned-out during thepandemic. The most common personal resources used to cope with additional stress were relyingon family members, friends, and colleagues for support, in addition to utilizing personal hobbiesto decompress. Lastly, participants were able to provide suggestions on how to improve jobresources, focusing on improving mental and physical support, communication, and retention.Conclusion: This study provides an in-depth understanding of the COVID-19 workingconditions of Ontarian obstetrical nurses, while highlighting that they were provided withinadequate levels of job resources to manage increased job demands. Findings from this studycan help inform hospital management on how they can better support and meet the needs ofthose working in maternal care during major disease outbreaks. À la demande de l'auteur, le résumé a été retiré en raison de la nature confidentielle de la thèse. Il sera ajouté une fois la période d'embargo terminée.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".