Discovering the untapped benefits of team nursing in an acute haemodialysis unit of a major teaching hospital
Bibliographic record
Abstract
Background: Nursing duties in a haemodialysis setting can be performed using two main models which are primary and team nursing. The study sought to determine the most appropriate nursing care model in an acute haemodialysis unit (AHU) of a large metropolitan teaching hospital where primary nursing was replaced by the team nursing model on a trial basis. Methods: Standard questionnaires were administered to nursing staff pre and post the introduction of team nursing to determine the effectiveness of primary and team nursing in our dialysis unit. Clinical charts were audited prior to the introduction of team nursing and three months after to detect deviations in appropriate standards of care. A descriptive statistical analysis of data was conducted to address the purpose of this study. Results: Staff took an average of 37±4 days to review patient needs prior to the utilization of the TNC model and 5±3 days 3 months later. Handing over of patients improved from 25% to 65% and 25% of charts audited prior to the TNC model had errors compared to 13% after the introduction of team nursing. On a score of 0 to 10, the success of the PNC model had a mean of 5.2±2.5 compared to 8.3±0.7 for the TNC model. Conclusion: The team nursing care model can be effectively applied in an acute haemodialysis setting without compromising patient care and the superiority of team nursing over primary nursing in this setting has also been reinforced.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".