Understanding Nursing Care Omissions and Assessment Instruments in Emergency Departments: A Scoping Review Protocol
Bibliographic record
Abstract
Introduction: The omission of nursing care, characterized by the failure to perform necessary tasks due to various constraints (Kalisch et al., 2009), is a critical issue affecting healthcare globally (Aiken et al., 2018). This phenomenon is particularly pronounced in emergency departments (EDs), where high patient acuity and workload challenges often lead to lapses in care delivery. Objective: This scoping review aims to map the literature on nursing care omitted in EDs and the instruments used to measure the omission of nursing care in this setting. Method: We will follow Joanna Briggs Institute guidelines for scoping reviews and report according to PRISMA-ScR recommendations (Peters et al., 2020). Eligibility criteria include studies with nurses working in EDs, focusing on omitted nursing care, using a measurement instrument and adopting quantitative, qualitative, or mixed-methods designs. Discussion and Research Spin-offs: This review will provide an overview of nursing care omissions in EDs and the instruments used to measure them, thus shedding light on research needs and practical implications for improving care quality and patient safety.
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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 | 0.109 | 0.113 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.041 | 0.008 |
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".