Model for Doctor of Nursing Practice Projects Based on Cross-Fertilization Between Improvement and Implementation Sciences: Protocol for Quality Improvement and Program Evaluation Studies
Bibliographic record
Abstract
BACKGROUND: Hundreds of nursing professionals graduate each year from Doctor of Nursing Practice (DNP) programs, entrusted with roles as practice scholars and leaders. Graduates are tasked to lead multidisciplinary knowledge implementation projects to improve safety, quality, and key performance metrics. Nevertheless, there is a continued lack of agreement and faculty dissatisfaction with the format, focus, and results of the DNP graduation projects. The use of a wide range of models and methodologies from different sciences for knowledge implementation introduces challenges to DNP students; affects the scientific rigor of the projects; and results in the overuse, superficial use, or misuse of the models. Quality improvement (QI) and program evaluation studies are substantial investments that may lead to waste and even harm if not well conducted. Traditional QI methodologies, commonly used in DNP projects, were found to be uncertain in improving health care outcomes. The complexity of health care systems calls for cross-fertilization between improvement and implementation sciences to improve health care outcomes. OBJECTIVE: This study describes the development, implementation, and evaluation of a hybrid model for QI and program evaluation studies to guide scholarship in the DNP program. METHODS: The hybrid model was based on cross-fertilization between improvement and implementation sciences. The model adapted the Getting to Outcome (GTO) and Knowledge to Action (KTA) models as the overarching process models for knowledge implementation. Within each phase of the GTO and KTA models, expected barriers and facilitators for the implementation and adoption of innovation were identified based on the CFIR (Consolidated Framework for Implementation Research). Accordingly, strategies to facilitate the implementation and adoption of innovations were identified based on a refined list of implementation strategies and QI tools. The choice of these models was based on the top 5 criteria for selecting implementation science theories and frameworks. Seven DNP students used the hybrid model to conduct QI projects. Students evaluated their experiences by responding to a Qualtrics survey. RESULTS: The hybrid model encouraged a comprehensive systematic way of thinking, provided tools essential to implementation success, emphasized the need for adaptability in implementation, maintained rigor in QI, and guided the sustainability of change initiatives. Some of the challenges faced by students included finding reliable and valid measures, attaining and maintaining staff buy-in, and competing organizational priorities. CONCLUSIONS: Cross-fertilization between improvement and implementation sciences provided a roadmap and systematic thinking for successful QI projects in the DNP program. The integration of the CFIR with the GTO or KTA process models, enforced by the use of evidence-based implementation strategies and QI tools, reflected the complexity of health care systems and emphasized the need for adaptability in implementation. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/54213.
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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 | 0.201 | 0.215 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.041 | 0.012 |
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".