An Outline of the History of Family Nursing in Japan and the Japanese Association for Research in Family Nursing (JARFN)
Bibliographic record
Abstract
The term family nursing first came to be known in Japan in 1993 when Chiba University and the University of Tokyo both started offering courses in family nursing. Although the family nursing curriculum at Chiba University was endowed for 5 years, Professor Kazuko Suzuki (who, since retiring from Tokai University as a professor, is currently running her research institute) and Hiroko Watanabe (who is currently directing the Family Care Research Institute) vigorously wrote textbooks and held an international symposium during this 5-year period, in an effort to popularize family nursing research throughout Japan. The symposium invited Professor Wright and Associate Professor Bell from the University of Calgary, and its participants included Professor Michiko Moriyama among others. These were truly the days in which family nursing was being introduced to Japan. Meanwhile, the University of Tokyo was steadily building a foundation for family nursing by hosting an official course taught by Professor Chieko Sugishita. Back then, Kazuko Ishigaki (currently the president of Ishikawa Prefectural Nursing University) was an associate professor and Naohiro Hohashi (presently a professor at Kobe University) was an assistant instructor. This course later had Noriko Yamamoto (presently a professor at University of Tokyo), Nami Kobayashi (presently a professor at Kitasato University), and others as its graduates.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".