Partners in research: building academic‐practice partnerships to educate and mentor advanced practice nurses
Bibliographic record
Abstract
RATIONALE: Clinical practice is the primary focus of advanced practice nursing (APN) roles. However, with unprecedented needs for health care reform and quality improvement (QI), health care administrators are seeking new ways to utilize all dimensions of APN expertise, especially related to research and evidence-based practice. International studies reveal research as the most underdeveloped and underutilized aspect of these roles. AIMS: To improve patient care by strengthening the capacity of advanced practice nurses to integrate research and evidence-based practice activities into their day-to-day practice. METHODS: An academic-practice partnership was created among hospital-based advanced practice nurses, nurse administrators, and APN researchers to create an innovative approach to educate and mentor advanced practice nurses in conducting point-of-care research, QI, or evidence-based practice projects to improve patient, provider, and/or system outcomes. A practice-based research course was delivered to 2 cohorts of advanced practice nurses using a range of teaching strategies including 1-to-1 academic mentorship. All participants completed self-report surveys before and after course delivery. RESULTS: Through participation in this initiative, advanced practice nurses enhanced their knowledge, skills, and confidence in the design, implementation, and/or evaluation of research, QI, and evidence-based practice activities. CONCLUSION: Evaluation of this initiative provides evidence of the acceptability and feasibility of academic-practice partnerships to educate and mentor point-of-care providers on how to lead, implement, and integrate research, QI and evidence-based activities into their practices.
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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 | 0.077 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.005 | 0.038 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".