Implementation of Evidence‐Based Practices in the Context of a Redevelopment Project in a Canadian Healthcare Organization
Bibliographic record
Abstract
PURPOSE: The recent introduction of a project management office (PMO) in a major healthcare center, led by a nurse, provides a unique opportunity to understand how a PMO facilitates successful implementation of evidence-based practices in care delivery. DESIGN: A case study with embedded units (individuals, projects, and organization). In this study, the case is operationally defined as the PMO deployed in a Canadian healthcare center. METHODS: The sources of evidence used in this study were diverse. They consisted of 38 individual interviews, internal documents, and administrative data. The data were collected from March 2009 to November 2011. Content analysis was used to analyze the qualitative data. FINDINGS: PMO experts help improve practices, and the patients thus receive safer and better quality care. Several participants point out that they could not make the changes without the PMO's support. They mention that they succeeded in changing their practices based on the evidence and acquired knowledge of change management with the PMO members that can be transferred to their practice. CONCLUSIONS: With the leadership of the nurse director of the PMO, members provide a range of expertise and fields in evidence-based change management, project management, and evaluation. CLINICAL RELEVANCE: PMO facilitates the implementation of clinical and organizational practices based on evidence to improve the quality and safety of care provided to patients.
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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 | 0.020 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".