Workplace stress in paediatric intensive care units in Saudi Arabia: A mixed-methods study
Bibliographic record
Abstract
Workplace stress, as experienced by nurses working in intensive care units, can affect health, quality and delivery of nursing care and healthcare costs. However, no studies have purely focused on Paediatric Intensive Care Units (PICU) and specifically considered workplace stress within a Saudi Arabian context. This study addressed this omission. This study explored workplace stress amongst nurses working in PICUs in Saudi Arabia. A mixed-method research was conducted in two phases. In Phase One, (n = 172) nurses from six PICUs completed a questionnaire; in Phase Two, face-to-face semi-structured interviews were conducted with 24 of the original 172 participants. The quantitative data revealed that workplace stress was associated with workload (2.29 ± 0.81), followed by death and dying (2.07 ± 0.77) alongside patients and their families (2.02 ± 0.79). Most nurses suffered from medium levels of workplace stress; this was associated with tangible personal characteristics, including nationality and academic nursing qualifications. Six key themes emerged from the qualitative results: Sources of workplace stress, consequences of workplace stress, individual characteristics that help to manage workplace stress, work characteristics that help to manage workplace stress, motivation to work in PICUs in Saudi Arabia and suggestions for workplace stress management. The Dynamic Model of Workplace Stress was developed, highlighting the interactions between the sources and consequences of workplace stress. Despite reporting a medium level of workplace stress, the nurses perceived their workplace to be a highly rewarding environment.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".