The Joint Venture Model of Knowledge Utilization: A Guide for Change in Nursing
Bibliographic record
Abstract
Knowledge utilization (KU) is an essential component of today's nursing practice and healthcare system. Despite advances in knowledge generation, the gap in knowledge transfer from research to practice continues. KU models have moved beyond factors affecting the individual nurse to a broader perspective that includes the practice environment and the socio-political context. This paper proposes one such theoretical model the Joint Venture Model of Knowledge Utilization (JVMKU). Key components of the JVMKU that emerged from an extensive multidisciplinary review of the literature include leadership, emotional intelligence, person, message, empowered workplace and the socio-political environment. The model has a broad and practical application and is not specific to one type of KU or one population. This paper provides a description of the JVMKU, its development and suggested uses at both local and organizational levels. Nurses in both leadership and point-of-care positions will recognize the concepts identified and will be able to apply this model for KU in their own workplace for assessment of areas requiring strengthening and support.
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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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".