Analysis of the particularities of Lusophone participation in a Nursing Knowledge Network
Bibliographic record
Abstract
Abstract Objective To describe the conceptual exercise of reflecting on the possibilities and particularities of the participation of Lusophone schools of nursing in the Nursing Knowledge Network. Method An analysis was conducted using information obtained from an environmental scan of institutional resources following the conceptual framework by Prug and Prusak on the knowledge networks. The learnings reported in the analysis are based on the collected information and reflections on the positive and negative aspects of participation, while proposing possible solutions for an action plan. Results There is interest in the internationalization of research and collaborative work both as institutional actions to support nursing research and potential benefits due to participation in the Network. The collaborative work has potential to increase the impact of research, expedite dissemination and use of results both in education and in clinical practice, broadening the horizons of Lusophone nursing science. Conclusion and Implications for practice Participation of these institutions in the Network offers numerous possibilities to demonstrate the originality, creativity and expertise of their teaching and research practice, encouraging the sharing of ideas and practices. The practice of scientific production in all its scenarios by educators and students can be improved through refined ways of thinking, creating, producing, and disseminating knowledge.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".