Coping strategies among acute and critical care nurses: a scoping review
Bibliographic record
Abstract
PURPOSE: In acute and critical care (ACC) settings, registered nurses (RNs) undertake multifaceted responsibilities, contributing to their susceptibility to workplace stressors. Despite the pivotal role of coping strategies in preserving RNs' well-being, challenges in cultivating effective mechanisms persist, underscoring the necessity for synthesized evidence to inform supportive interventions. Therefore, in this review, we aim to summarize the international literature on the coping strategies that RNs in ACC use to manage workplace stressors. SOURCES: This was a scoping review adhering to the Joanna Briggs Institute methodology. Search databases included MEDLINE®, Embase®, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science®, and the Cochrane Library. Inclusion criteria included RNs (participants), coping strategies for work-related stressors (concepts), and global ACC settings (context). Documents prior to 2000 or those focused on various health care professionals/students, without differentiation of results, were excluded. Two reviewers independently screened, extracted, appraised, and analyzed the studies. PRINCIPAL FINDINGS: Out of 7,985 documents located, 168 relevant documents from 2003 to 2023 were included in this review. Among those were studies using intervention-based, quantitative descriptive, qualitative, mixed method, and other methodologic approaches. Ultimately, 3 categories emerged: 1) recognition of stressors, 2) coping strategies, and 3) influences on RNs' coping strategies. Coping strategies were further categorized into problem-focused, emotion-focused, meaning-focused, and support-seeking strategies, revealing a range of techniques used by RNs. CONCLUSIONS: Our findings provided valuable insights into the stressors faced by RNs in acute care settings and the coping mechanisms they employ, underscoring the importance of organizational support and tailored interventions in mitigating workplace stress and promoting the well-being of health care professionals.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".