Educational background of nurses and their perceptions of the quality and safety of patient care
Bibliographic record
Abstract
BACKGROUND: International health systems research confirms the critical role that nurses play in ensuring the delivery of high quality patient care and subsequent patient safety. It is therefore important that the education of nurses should prepare them for the provision of safe care of a high quality. The South African healthcare system is made up of public and private hospitals that employ various categories of nurses. The perceptions of the various categories of nurses with reference to quality of care and patient safety are unknown in South Africa (SA). OBJECTIVE: To determine the relationship between the educational background of nurses and their perceptions of quality of care and patient safety in private surgical units in SA. METHODS: A descriptive correlational design was used. A questionnaire was used for data collection, after which hierarchical linear modelling was utilised to determine the relationships amongst the variables. RESULTS: Both the registered- and enrolled nurses seemed satisfied with the quality of care and patient safety in the units were they work. Enrolled nurses (ENs) indicated that current efforts to prevent errors are adequate, whilst the registered nurses (RNs) obtained high scores in reporting incidents in surgical wards. CONCLUSION: From the results it was evident that perceptions of RNs and ENs related to the quality of care and patient safety differed. There seemed to be a statistically-significant difference between RNs and ENs perceptions of the prevention of errors in the unit, losing patient information between shifts and patient incidents related to medication errors, pressure ulcers and falls with injury.
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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 | 0.002 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".