The Impact of Patient to Nurse Ratio on Quality of Care and Patient Safety in the Medical and Surgical Wards in Malaysian Private Hospitals: A Cross-sectional Study
Bibliographic record
Abstract
Background and objective: Nursing shortage and inadequate hospital nursing jeopardized quality of care andpatient safety. This study aims to predict the impact of patient to nurse ratio on quality of care and patient safetyin medical and surgical wards in Malaysian private hospitals.Methods: Cross-sectional data collected by questionnaire from 652 nurses working in the medical and surgicalwards in 12 private hospitals participated in the study. Stratified simple random sampling performed to invitesmall size (less than 100 beds), medium size (100-199 beds) and large size (over than 200) hospitals’ nurse toparticipate in the study, which allow also nurses from all shift to participate in the study from the participatedhospitals.Results: Nurses with higher ratio of patients have greater negative association on quality of care and patientsafety. However, this negative association significantly associated with patient safety, whereas insignificantlyassociated with quality of care.Conclusions: Staffing level inconsistently associated with quality of care and patient safety, so there is at leastone intervening process factor mitigate the negative impact of nursing shortage on quality of care in Malaysianhospitals. However, nurses delivering care for 11-15 patients and nurses delivering care for more than 15patients had significant negative impact on both quality of care and patient safety at a p<0.05 significance levelcompared with those caring for less than 5 patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".