Practice environment, job satisfaction and burnout of critical care nurses in South Africa
Bibliographic record
Abstract
AIM: To describe the practice environment, job satisfaction and burnout of critical-care nurses (CCNs) in South Africa (SA) and the relationship between these variables. BACKGROUND: CCNs are more sensitive to job satisfaction and burnout, and several studies have been published on the relationship between these variables. However, the research that was undertaken did not focus exclusively on the practice environment of CCNs or the context of SA. METHOD: The RN4CAST survey was used. A stratified sample of 55 private hospitals and seven national referral hospitals were included in the study. A total of 935 CCNs completed the survey. RESULTS: The practice environment is positive, except for staffing and resource adequacy, and governance. The greatest job dissatisfaction is experienced with regard to wages, opportunities for advancement and study leave. CCNs have a high degree of burnout. CONCLUSION: The high degree of burnout is related to dissatisfaction with wages, opportunities for advancement, study leave and a practice environment with inadequate staffing and resources, and lack of nurse participation in hospital affairs. IMPLICATIONS FOR NURSING MANAGEMENT: Managers should ensure that adequate numbers of CCNs are on the staff allocation and provide opportunities for CCNS to participate in policy and governance of the hospital, while giving attention to good salaries and providing opportunities for advancement and study leave.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".