DEVELOPMENT OF A SCORING SYSTEM FOR THE NURSING WORKLOAD IN A PEDIATRIC INTENSIVE CARE UNIT
Bibliographic record
Abstract
Abstract BACKGROUND Nursing workload evaluation tools are designed to determine adequate staffing for a given shift. Only retrospective tools that do not predict the number of nurses needed to start a shift exist. A prospective nursing workload evaluation tool (SJ score), developed by a group of nurses with items based on previously published retrospective scores and clinical experience, includes 16 weighted sections (scored from 0 to >100 with 1 point ≈ 5 min nurse workload). OBJECTIVES This study’s aim is to assess the reliability and validity of the SJ score in the Paediatric Intensive Care Unit (PICU). DESIGN/METHODS Inclusion criteria: children admitted in a PICU, age < 18 yo. Exclusion criteria: already included 3 times in this study (phase 1 only) or children discharged 2 hr after the beginning of the nurse shift studied. Children were scored for 8 hr nursing shifts. Phase 1 (pilot validation) required simultaneous prospective SJ scoring by the nurse in charge (NIC) and chief nurse (SC), then a retrospective SJ score by an independent trained investigator (AE). Phase 2 (validation in the real context of the PICU), which used an improved SJ score, required that each child had an SJ score prospectively by the NIC of the previous shift, then retrospectively by the NIC of the dedicated shift. Statistical analysis included the intraclass correlation (ICC) and a Bland Altman plot. Bland Altman was considered acceptable if mean difference was closed to 0. For ICC: 0.40 RESULTS 165 patients’ shifts observations were performed in phase 1. In the comparison between the prospective score performed by the NIC and SC, the Bland Altman mean difference was -0.03 with limits of agreement between -3.63 and 3.58, and the ICC was good: 0.63 with 95%confidence interval (95ICC) from 0.40 to 0.93. In the comparison between the prospective score of the NIC and AE retrospective score, the ICC was fair: 0.52 with 95ICC from 0.32 to 0.78. In phase 2, 2599 patients’ shifts were studied. The Bland Altman mean difference was 0.21 with limits of agreement between -10.5 and 10.9, and the ICC was excellent: 0.86 with 95ICC from 0.85 to 0.87. CONCLUSION The SJ score prospectively predicted well nursing workload in a single PICU. Additional studies are needed to determine the validity in other PICUs.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".