Stress and coping strategies among nurse managers
Bibliographic record
Abstract
Background: The role of Nurse Managers (NMs) is dynamic, multifaceted and complex thus, exposing NMs to high levels of work-related stress which seriously impact general wellbeing, and organizational outcomes.Methods: A quantitative cross-sectional approach was employed to examine the phenomenon of stress among NMs in 38 selected hospitals. Census approach was used to collect data from 267 NMs. Descriptive and inferential statistics were performed to describe the sample and established the predictors of stress.Results: The main causes of stress among NMs are a shortage of staff (94.4%), poor working conditions (91.8%), inadequate management support (89.9%) and heavy workload (89.15%). NMs experienced all the types of stress (psychological, emotional and physical). The major stress coping mechanisms are time management (91.8%), effective communication (91%) and delegation of duties (89.5%) while excessive eating (18.4%) is the least strategy used. Sociodemographic characteristics together explained 6.4% of stress among NMs [R2 = .064, F(6,241) = 2.676, p = .016].Conclusions: Senior managers of hospitals should create a favourable working environment for nurses and the appointment of NMs should be based on experience and competence. Implication for Nursing Practice: Stress among healthcare managers especially, NMs is very common. This current study has extensively proven that stress among NMs affects their general health as well as patient safety and quality of care. Training on stress management should be organized regularly for hospital staff particularly, NMs to enable them to cope better with stress.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".