Conducting research through cross national collaboration
Bibliographic record
Abstract
AIM: To explore the collaborative nature of an international research project with other advanced practice nurse researchers and critically analyse the process. BACKGROUND: Research within the nursing community is recognized internationally as important to ensure that nurses participate in cutting-edge health care and promote evidence-based practices, yet there is little detail found in literature on how a successful collaborative relationship is initiated and conducted in advanced practice research. DESIGN: Discussion paper: The purpose of this paper is to discuss the process of collaboration on a research study among advanced practice nurses from four countries who are members of an international organization. IMPLICATIONS FOR NURSING: The collaborative process in international nursing research can be challenging and rewarding. It is important to remember that there is a relationship between the complexity of the study and the time required to complete it. Keys to success include following established guidelines. CONCLUSION: This project was a valuable experience in developing collaborative relationships as well as creating partnerships for future research to build on the knowledge gained. The authors' linkages to universities facilitated their participation in the research and completion of the ethical review processes. The use of social media and university resources was indispensable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.338 | 0.268 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.003 | 0.037 |
| Research integrity | 0.005 | 0.008 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".