Scanning resources to build an international nursing knowledge network
Bibliographic record
Abstract
This paper reports the gathered information from an international environmental scan of university-baseline information for the creation of a Nursing Knowledge Network. An online environment scan of organizational context (February to October 2021) explored the identification of research areas, existing resources, expected benefits, innovations in teaching research, barriers to knowledge dissemination, and prospective contributions of the Network. Target informants were 200 nursing faculty affiliated to 63 universities located in 13 countries, as well as nursing networks in the Ibero-American context. One informant per university was asked to respond to the questionnaire. The participation rate was nearly 70% (42/63). The informants’ universities per country included Brazil (n = 21); Canada (n = 4); Portugal and Spain (n = 3 each); Colombia, Mexico, Peru, and USA (n = 2 each); Chile, Italy, and Paraguay (n = 1 each). Nursing faculty provided rich information and shared knowledge confirming a strong commitment to global co-creation of innovations and research partnership capacities through collaboration, cooperation, and knowledge exchange among nursing higher education institutions. Seldom researched areas are a potential focus for the Network to generate appropriate evidence to inform local scientific practices. The gathered information will inform further review of nursing and governmental policies and programs related to the application and dissemination of nursing evidence across local, regional, and global levels.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".