The Role of Nurse Implementation Scientists in Leading Health System Transformation in Atlantic Canada and Beyond: A Discussion Paper
Bibliographic record
Abstract
AIM: To discuss and provide examples of how nurse implementation scientists can support health system transformation. DESIGN: Discussion paper. METHODS: Using Prince Edward Island's health system strategic plan as a case exemplar, selected key priorities in the strategic plan were mapped with examples and discussion of how nurse implementation scientists can support health system transformation on Prince Edward Island and beyond. CONCLUSION: Accelerating the development and delivery of evidence-informed services that support health system transformation is needed. Nurse implementation scientists are ideally positioned to lead these efforts. Appropriate resourcing and compensation are essential to fully embrace nurse implementation scientist roles, collaboration, and buy-in from health system leaders. IMPLICATIONS FOR NURSING: Practical examples of how nurse implementation scientists can lead health system transformation in a rigorous, evidence-informed way are identified. IMPACT: Literature providing examples of how nurse implementation scientists can make meaningful impacts within health systems, particularly in rural contexts, is limited. Nurse implementation scientists are ideally positioned to collaborate with and lead health system transformation by virtue of their knowledge, skills, qualifications and experience. Implications of this work extend beyond nursing to other health disciplines, health organisations, government leaders, researchers and populations. The discussion and examples provided may be applicable to similar contexts. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.
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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 | 0.034 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| 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".