Building an International Partnership to Develop Advanced Practice Nurses in Anesthesia Settings: Using a Theory-Driven Approach
Bibliographic record
Abstract
The International Federation of Nurse Anesthetists is calling for international collaboration to develop advanced nursing practice in anesthesia settings globally. However, international collaboration is challenging. Limited information is available about what process and factors specifically lead to a successful international collaboration partnership. This article aimed to describe a theoretical and empirical base that can be used to build and maintain long-term international partnerships. The Theoretical Framework of Developing International Partnerships was developed, which comprises seven interrelated concepts including partnerships, collaborations, environment, structure, process for collaborating, outcomes, and sustainability. It was used to guide an equitable horizontal collaboration partnership to develop anesthesia nursing care in local culture and context. Five major challenges were identified during the collaboration process. Sixty-six strategies were developed to facilitate collaboration using the theoretical framework. This work can inform others in establishing an international collaboration and partnership in advancing nursing knowledge and culturally congruent health care delivery.
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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 | 0.052 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".