Nursing activities and factors influential to nurse staffing decision-making
Bibliographic record
Abstract
Objective: There is limited published research supporting the effectiveness of nursing workload measurement to comprehensively measure nursing workload and to formulate nurse resource need. Predictive accuracy is impaired due to variation in direct and indirect care-related activities across measurement instruments. This study aimed to (1) identify common nursing activities considered by nurse managers for staffing decision-making, (2) systematically review such nursing activities in relation to existing nursing workload instruments and Nursing Intervention Classification taxonomy, and (3) describe challenges perceived by managers in staffing decision-making.Methods: A survey was developed from an inclusive review of 20 nursing workload instruments collectively measuring 502 nursing activities. Nurse managers in 13 medical-surgical and two intensive care units at a Midwest healthcare organization identified nursing activities considered daily for staffing decision-making.Results: Twenty-one activities were commonly considered by at least 90 percent of managers (n = 13) for daily staffing decisionmaking, although none of the instruments reviewed included all 21 activities.Conclusions: Lack of a standardized framework for nursing workload measurement might have led to nurse managers’ different perceptions about appropriate determinants of these measurements. A standardized approach for measuring nursing workload would facilitate benchmarking for estimating nurse resource need. Further research is needed to design a systematic infrastructure that ensures staffing to meet patient care need. A process is also needed to alleviate the challenges in staffing decision-making that nurse managers face, such as fluctuations in census and patient acuity, nurse competency-based patient assignments, and limited information resources for staffing estimation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".