Up, dressed and moving: how nurses are employing evidence to transform patient care
Bibliographic record
Abstract
To paraphrase Benjamin Disraeli, the secret of success is to be ready when your opportunity comes. In 2019, the International Council of Nurses (ICN) stated that “nurses are essential in transforming health care and health systems such that no person is left behind, without access to care or impoverished because of their need for health care.”1(para.1) Across the world, examples of transformation by nurses can be found that truly make a difference to people's lives. These differences may be large or small in scale, but regardless, for the people concerned, they are life-enhancing. Bringing about change requires people to think and behave differently; this can be quite a challenge. However, by understanding the factors associated with successful implementation of new ideas or practices, we can be far better prepared to act when the opportunity presents and the timing appears right. One idea that has transformed health care and gone viral is #EndPJParalysis.2 The campaign, led by a nurse, Professor Brian Dolan, has become a global social movement embraced by nurses, therapists and medical practitioners, and aims to get patients up, dressed and moving. These simple activities have been found to shorten hospital stays, reduce falls and enhance wellbeing. The campaign has made a significant difference to how we care for people in hospital, with many countries worldwide taking up Professor Dolan's challenge and transforming lives. These ideas were shared with an audience who listened, understood why change was needed, and accepted the challenge by changing their behavior to make a difference. Each year, nurses from across the world acknowledge the contributions to the profession by an early leader in nursing, Florence Nightingale, by celebrating International Nurses Day on May 12, which was Nightingale's birthday. The theme of International Nurses Day for 2019, set by the ICN, addresses the leadership role of nurses by focusing specifically on the challenges related to achieving “Health for all”. This theme is reflected in the Joanna Briggs Institute's vision: “a world in which the best available evidence is used to inform decision making at the point of care to improve health outcomes in communities globally”. Nurses are key players in achieving this, in terms of generating, implementing and embedding evidence that can be used to underpin decision making. In this month's issue of the journal, we see how evidence synthesis is being used to establish the best available evidence for three very different aspects of health care of direct relevance to nursing: handover,3,4 workplace violence5 and compassion fatigue.6 Each of these impacts health in different ways in every part of the world. Nurses working in all types of healthcare settings across the world rely on information being communicated effectively from one team of people to another to enable the delivery of health and social care. Good communication is vital for the effective transfer of information, and evidence indicates it is a critical component of patient safety; however, its importance is often underestimated.7 Practices associated with daily interprofessional handovers and shift-to-shift handovers have changed in recent years, and they are (slowly) becoming more evidence-informed. We have seen more and more evidence of patients actively being included in these communication exchanges, with some informative research producing significant contributions to the body of knowledge in this area by Dawn Stacey et al.8 These researchers from Ottawa, Canada, have developed a decision-making aid for use with patients in an effort to translate evidence into a useful tool for clinicians. In some acute services, bedside discussions and care planning have been introduced, using a team-based, interprofessional approach that is now generating a growing evidence base.9,10 Many of these also involve the patient in this shared decision-making process.11 The culture of care is changing, and this creates opportunities for transformation to occur. Nurses need to respond to the call by the ICN and truly be the voice that leads the changes needed to achieve a world in which no person is left wanting for care. As a profession, we need to call on all nurses to be patient advocates, to use our hard-earned scientific reasoning skills and abilities to transform practice, and take advantage of our large numbers to cascade this vision far and wide.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".