The STTI Practice‐Academe Innovative Collaboration Award: Honoring Innovation, Partnership, and Excellence
Bibliographic record
Abstract
PURPOSE: To describe the benefits and barriers associated with practice-academe partnerships and introduce Sigma Theta Tau International's (STTI's) Practice-Academe Innovative Collaboration Award and the 2009 award recipients. DESIGN AND METHODS: In 2008, STTI created the CNO-Dean Advisory Council and charged it with reviewing the state of practice-academe collaborations and developing strategies for optimizing how chief nursing officers (CNOs) and deans work together to advance the profession and discipline of nursing. The Council, in turn, developed the Practice-Academe Innovative Collaboration Award to encourage collaboration across sectors, recognize innovative collaborative efforts, and spotlight best practices. A call for award submissions resulted in 24 applications from around the globe. FINDINGS: An award winner and seven initiatives receiving honorable mentions were selected. The winning initiatives reflect innovative academe-service partnerships that advance evidence-based practice, nursing education, nursing research, and patient care. The proposals were distinguished by their collaborators' shared vision and unity of purpose, ability to leverage strengths and resources, and willingness to recognize opportunities and take risks. CONCLUSIONS: By partnering with one another, nurses in academe and in service settings can directly impact nursing education and practice, often effecting changes and achieving outcomes that are more extensive and powerful than could be achieved by working alone. CLINICAL RELEVANCE: The award-winning initiatives represent best practices for bridging the practice-academe divide and can serve as guides for nurse leaders in both settings.
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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 | 0.047 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.003 | 0.036 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".