Validation of a tool to measure neonatal nursing workload
Bibliographic record
Abstract
AIMS: To test the validity and reliability of the Winnipeg Assessment of Neonatal Nursing Needs Tool (WANNNT). BACKGROUND: Workforce planning is increasingly challenging. Existing tools can be inadequate. METHODS: Nurses provided estimates of patient care time. Charge nurses assessed overall safety of care. Patient levels were compared between two independent assessors. Total nursing needs was compared between the WANNNT and an independent charge nurse. RESULTS: Mean time estimates for levels 1-5 were not significantly different from the WANNNT. Nurses estimated 50% less time than the tool assigned level 6. The tool was 95% reliable in assigning patient levels between two assessors. The mean difference between the total WANNNT assessment and the charge nurse was one nurse. CONCLUSIONS: The tool provided a reasonable and reliable estimation of the number of nurses required for a given collection of patients in order to provide the highest quality of care. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse managers incorporating this tool need to determine the additional drivers for nursing time that must be considered which may be unique to their unit or hospital. The WANNNT will be a valuable asset for making staffing decisions on a shift to shift and long-term basis.
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 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 | 0.044 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".