Major hospital transformations: An integrative review and implications for nursing
Bibliographic record
Abstract
Major hospital transformations, hospital projects that combine construction and quality improvement dimensions, are booming around the globe. These costly endeavours have the potential to revolutionize healthcare, yet no known review explores this phenomenon, undermining accessibility of knowledge for healthcare leaders. In order to provide guidance on healthcare project management and on future research avenues, this article aims to synthesize empirical knowledge concerning major hospital transformations and their implications for nursing. An integrative review of the literature using the systematic approach described by Whittemore and Knafl was selected. As major hospital transformations represent a new area of research, the review includes 13 articles out of 116 retrieved for screening. The search strategy included the following electronic databases: CINAHL, MEDLINE, and Business Source Complete. Three main themes emerged from the data: the challenging context of major hospital transformations, the project management office as a key to successful healthcare change, and the absence of certain stakeholders’ voices. Major hospital transformations are important to study holistically as multi-change initiatives cannot be understood through investigating individual changes alone. Healthcare leaders are called to reflect on their governance structures during organisational transformations, as well as on the inclusion and exclusion of certain stakeholders who are essential to making sustainable change.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".