Advocating for patient safety: Power dynamics in nurse advocacy practice in Australia—An integrative review
Bibliographic record
Abstract
Introduction Nurses play a vital role in advocating for patient safety, yet their ability to fulfil this role is influenced by the power dynamics within healthcare systems. Understanding the relationship between power and nurse advocacy in Australia is essential to fostering a supportive environment for effective advocacy. Aim This integrative literature review aims to identify and examine the relationship between power and the advocacy role of nurses in Australia. Methods The review followed an integrative literature review design, guided by the approach outlined by Toronto and Remington. A comprehensive search was conducted in electronic databases such as Medline, CINAHL, Emcare, Scopus, ProQuest Health & Medicine, and Informit. The search terms used were ‘nurse OR nursing OR nurses' AND ‘advocacy OR whistleblowing' AND ‘Australia.' A total of 2507 articles were retrieved, and 26 studies met the inclusion criteria, comprising 18 qualitative studies, one quantitative study, one mixed-method study, one review of existing literature, and four editorial commentaries. The search was completed in May 2023. Findings The findings suggest that enhancing nurses' advocacy for patient safety requires a multifaceted approach. This includes empowering nurses through professional development and leadership opportunities, fostering a culture of patient safety, and engaging in political action to advocate for policies that support advocacy efforts and patient safety. This approach aims to advance patient well-being and elevate the professional standing of nurses within the healthcare system. Discussion Power dynamics significantly shape nurse advocacy practices. Nurses with greater personal power are more likely to advocate confidently, while those with less power may be hesitant. Healthcare organisations can either support or hinder advocacy efforts, with unsupportive systems creating barriers and fostering a culture of silence. Whistleblowing, as a form of advocacy for patient safety, is also affected by organisational culture and power structures. Conclusion Power dynamics play a critical role in determining how effectively nurses can advocate for patient safety. Empowering nurses and addressing organisational barriers are crucial for promoting advocacy in healthcare. This review highlights the need for healthcare systems to cultivate environments that support and facilitate nurse advocacy.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".