How does feedback shared with interprofessional health care teams shape nursing performance improvement systems? A rapid realist review protocol
Bibliographic record
Abstract
BACKGROUND: Nursing care quality varies between hospitals, and even between departments within the same institution. Suboptimal care can have deleterious consequences for patients such as lengthened hospital stay, nosocomial infection, pressure ulcers or death. Experts recommend the implementation of nursing performance improvement systems to assess team performance and monitor patient outcomes and efficiency savings. In practice, these systems are expected to include feedback processes directed towards nursing teams and interprofessional staff in order to facilitate adjustments and improve their performance. Unfortunately, feedback appears somewhat haphazard and, at times, overlooked. This could be explained by an ongoing absence of clear recommendations. As a result, feedback effects are inconclusive: some teams improve their practice, others do not. Although feedback has been conceptualised and studied from different theoretical perspectives, ongoing empirical inconsistencies remain unexplained. The goal of this rapid realist review protocol is to develop a theory that explains how feedback shared with interprofessional health care teams shape nursing performance improvement systems. METHOD: This study follows standard guidelines established for realist reviews. Mechanisms at work will be analysed using Actor-Network Theory. All scientific documents are selected from five databases, are published in both English and French between 2010 and 2018, and include empirical research, reviews and grey literature. First, selection of documents will proceed on the basis of titles and abstracts; followed by a second selection by reading the remaining full texts. Inclusion criteria and a data extraction form will be pilot tested with 40 articles prior to completion by two reviewers. Data will be summarised in the form of [context, mechanism, outcome] equations to theorise operational feedback. DISCUSSION: The innovative combination of Actor-Network Theory with a realist methodology holds promise for the identification of explanatory equations in complex systems and theory development. A rapid realist review is relevant to address an enduring knowledge gap which requires theory development. This preliminary study lays the groundwork for a pioneering theory on feedback in nursing performance improvement systems that will subsequently inform a multiple case study. SYSTEMATIC REVIEW REGISTRATION: Prospero CRD42018110128.
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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 | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".