An integrative review of the influence of job strain and coping on nurses' work performance: Understanding the gaps in oncology nursing research
Bibliographic record
Abstract
Nursing is known to be a stressful profession that can lead to physical and psychological health issues and behavioural problems. In oncology, workload among nurses is believed to be increasing in conjunction with rapidly increasing numbers of patients with cancer and staff shortages worldwide, therefore it is essential to sustain a quality oncology nurse workforce. Numerous studies have presented evidence on job strain, effects of coping strategies, and nurses' work performance within healthcare settings, but few have focused on oncology settings and none of these on nurses working in Saudi Arabia. The purpose of this review was to summarize empirical and theoretical evidence concerning job-related stressors in nurses, particularly oncology nurses, and the interrelationships among job strain, coping strategies, and work performance in this population. Search strategies identified studies published on studies in peer-reviewed journals from 2004 to 2016. Twenty-five nursing studies were found examining the relationships among the concepts of interest. Common job-related stressors among oncology nurses were high job demands, dealing with death/dying, lack of job control, and interpersonal conflicts at work. Job strain was found to be significantly linked to coping strategies, and negatively associated with work performance among nurses in general. There is no existing empirical evidence to support the relationship between coping strategies and work performance among oncology nurses. The present evidence is limited, and a considerable amount of research is required in the future to expand the oncology nursing literature. Research is needed to investigate job-related stressors and their effects on oncology nurses.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".