Coping with clinical related stress experienced by undergraduate nursing students: A scoping review
Bibliographic record
Abstract
• Clinical learning experiences may be stressful for undergraduate nursing students. • The most frequently used coping mechanism was problem-focused approaches. Coping strategies employed by students were often conceptualized as dichotomous, such as effective or in-effective, rather than dependent on contextual circumstances. • Coping as a concept was primarily explored using measurement tools that limited understanding the processes students used to engage in problem-solving. • Further empirical evidence is needed to equip students with personalized tools and skills that can be used to manage stressors during experiential learning experiences. To identify the strategies used by undergraduate nursing students to cope with clinical related stress. Learning in the clinical environment may be stressful for nursing students. Effective coping mechanisms are imperative given that stress may compromise students’ well-being, ability to learn, and patient care. A scoping review was conducted using the Joanna Briggs Institute framework. Primary research sources and dissertations were searched using the CINHAL, PubMed, PsycINFO, Google Scholar, and Thesis Global databases as well as an ancestry approach. Eligibility criteria included primary research on undergraduate nursing students’ coping with clinical related stress in an experiential learning environment. From a total of 573 identified articles, 35 met the inclusion criteria. The included studies were primarily conducted using quantitative designs, originated from various countries, and focused on students’ clinical experiences from varying years. The most frequently used coping mechanism was problem-focused approaches. Coping strategies employed by students were often conceptualized as dichotomous, such as effective or in-effective, rather than dependent on contextual circumstances. Coping as a concept was primarily explored using measurement tools that limited understanding the processes students used to engage in problem-solving. This review highlighted a gap in the trajectory of “how” students cope with stress in clinical practice. Further research is needed to inform the development of personalized tools and skills that can be used by students.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 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".