Applying a balanced score card approach and Multi-Rater feedback strategy to shift from appraising to managing head nurses’ performance at general surgical units-Main Mansoura University Hospital-Egypt
Bibliographic record
Abstract
Background and objective: Traditionally, performance appraisal is used to measure behaviors, procedures or actions taken as in head nurse performance plans. Applying balanced score card measures at the nursing administration department level with participating the findings with all nursing staff and patients will help administrators to get all information needed to match head nurses performance plans with nursing administration department as well as hospital goals which help in drawing full shape of performance. The objective was to apply balanced scorecard approach with 360-degree feedback strategy to shift from appraising to managing head nurses’ performance at general surgical units -Main Mansoura University Hospital-Egypt.Methods: Subjects: All supervisors (n = 12), head nurses (n = 10), staff nurses (n = 96) working in general surgical units and all available patients admitted to these units at the time of study (n = 113) were included. Tools: Eight tools that were used for data collection involved feedback questionnaires for patients, head nurses, staff nurses and supervisors, observational checklist, activity analysis checklist, auditing performance appraisal form, and auditing personnel decisions form.Results: There was a statistically significant difference between head nurses, staff nurses and supervisors’ perspectives from one side and among patients’ perspectives from the other side regarding the performance of head nurses’ in general surgical units. In addition, nearly of third of head nurses’ time spent in unclassified activities. However, none of personnel decisions depended on performance appraisal outcomes.Conclusions: The results of the present study indicated that the methods used to measure head nurses’ performance in general surgical units at Main Mansoura University Hospital were not integrated or depend on clear work standards to develop and improve the performance of Head Nurses (HNs).
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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 | 0.073 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".