Diffusion of Innovations-Informed Knowledge Translation Strategy to Implement Optimal Safe Nursing Workforce Policy in Practice
Bibliographic record
Abstract
AIM: This study aims to develop a free, limited-edition workshop as an effective knowledge translation strategy to enhance nurse leader-perceived self-efficacy for competence using Park’s Sweet Spot Theory and to evaluate its effectiveness over time. METHOD: This is a study showing the process of developing a study protocol and its details. RESULTS: A 2-day workshop was developed for innovators and early adopters among nurse leaders with a macro-level influence based on Rogers’s diffusion of innovations theory, which consists of an introduction of Park’s Sweet Spot Theory, hands-on experience, a summary session, and a presentation of a certificate of completion. The workshop will be held at the University of Alberta Faculty of Nursing, using the “enabling blends” mode. A hybrid design of comparative effectiveness research and analysis of change will be utilized to assess nurse leader-perceived self-efficacy. CONCLUSION: This protocol is significant as the first step in providing scientific rationales on how to effectively implement new knowledge—optimal safe nurse staffing levels derived from Park’s Sweet Spot Theory—into the right (safe yet efficient) nursing workforce policy-making to alleviate global nursing shortages. Cite this article as: Park, C. S., Lee, S., Kim, H., & Kabak, M. (2022). Diffusion of innovations-informed knowledge translation strategy to implement optimal safe nursing workforce policy in practice. Florence Nightingale Journal of Nursing, 30(1), 92-99.
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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 | 0.036 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".