Exploring educational program for specialisation of the primary care nursing role
Bibliographic record
Abstract
In countries including Australia, Canada, Ireland, the UK and Spain, primary care nursing (PCN) is situated as a part of the nursing education but it is not well recognised in Japan. As Japan's society continues to age, there is a shift underway from hospital to community care and the role of nurses in the community is arguably more important than ever before. There are misconceptions around the role of primary care nurses, including the incorrect assumption that their role is that of a doctor's assistant. Rather, their roles are far more complex and require a people-centred approach. Dr Mayumi Kako, School of Biomedical and Health Sciences, Division of Nursing Science (International Disaster Nursing), Hiroshima University, Japan, is working to improve understanding of and appreciation for the role of primary care nurses. This research involves developing educational programmes to improve awareness of the professional role of PCN. She and her team are drawing comparisons between the concept of PCN in the basic nursing educational curriculum in Japan and PCN education in Australia, Canada, Spain, UK, and Ireland in order to identify contrasts and enhance awareness. If they can better understand how the PCN concept is positioned in other countries, they will be able to identify gaps between PCN curriculums in these countries and Japan, and investigate how the PCN concept could be embedded into the Japanese nursing curriculum in the future.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".