Nurses and Unlicensed Assistive Personnel's Practices in Caring for Patients With Delirium in Acute Care Settings: Protocol for the PRACTICE Study
Bibliographic record
Abstract
Introduction: Few studies have investigated nursing practice in relation to delirium in acute care settings, and no studies have investigated the care of unlicensed assistive personnel (UAPs) in this context. As a result, it becomes challenging to support the delivery of optimal care and thereby improve delirium-related patient outcomes. Objective: This manuscript reports on the development of two survey tools and a study protocol that aims to (1) describe the current practices of nurses and UAPs in the context of nursing care in delirium and to (2) highlight the barriers and facilitators to the delivery of optimal delirium care. Methods: This multi-method study aims to recruit nurses and UAPs. During an initial quantitative phase, participants will answer two survey tools designed respectively for nurses and UAPs. These tools were developed using a modified Delphi technique and a guide based on Burns et al. (2008) and Eysenbach (2004). They examine delirium knowledge, practice, collaboration, confidence, and the impact of the COVID-19 pandemic on practice relatively to delirium. Descriptive and inferential statistical analyses will be performed on this data. The qualitative phase will include focus groups and interviews with nurses and UAPs to explore topics from the survey tools more in-depth. Thematic analysis will be performed on the transcripts. Data from both phases will answer the two study aims. Discussion and Research Spin-offs: This study will be the first to report on the delirium care offered by UAPs. The survey tools developed can identify nurses’ and UAPs’ practices, and the barriers and facilitators to optimum nursing care for people with delirium.
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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 | 0.122 | 0.093 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".