Nursing care providers’ perceptions on their role contributions in patient care: An integrative review
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To explore registered nurses', licensed practical nurses' and healthcare aides' perceptions of their own and each other's role contributions. BACKGROUND: In response to contemporary economic and political pressures, healthcare institutions across the world have endeavoured to download job duties to less educated healthcare providers. As a result, nursing care is usually delivered by a team of nursing staff that have different roles. This means that there are fewer registered nurses and more licensed practical nurses and healthcare aides on nursing teams, despite evidence that increased numbers of registered nurses improve patient safety and care outcomes. DESIGN: This study was an integrative review using Whittemore and Knafl's stages for ensuring rigour. These stages include problem identification, literature searching, data evaluation, data analysis and presentation. METHODS: Four electronic databases were searched according to previously designed search strategies. The 14 retrieved articles were appraised using MMATs for quality. Data were extracted and analysed thematically. RESULTS: The findings of the integrative review revealed that registered nurses, licensed practical nurses and healthcare aides had little understanding about the roles of their fellow nursing team members and had difficulties describing their own roles. However, no studies concurrently examined registered nurses', licensed practical nurses' and healthcare aides' perceptions on their own or each other's roles and little were written about licensed practical nurses. CONCLUSION: More research is needed to examine the entire nursing team's perceptions about the various nursing roles.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".