Moving Care to the Community: An International Perspective
Bibliographic record
Abstract
Medical treatments that were once provided in hospital are being increasingly administered in the community. Within health systems, there is a renewed focus on delivering general health care in the community, freeing hospitals to provide more complex, specialised and emergency care. As the drive to shift specialised and non-specialised care out of hospital gathers momentum, there is a greater demand for a skilled and competent community nursing workforce to facilitate this shift at a local level. Nurses are essential in the delivery of continuous care as they often serve as an interface between acute and community care, focusing on prevention, self- management and providing support to transition patients smoothly across the health and social care services.Moving care to the community has been a UK-wide health and social care policy priority for more than a decade. However, progress has been slow and in some cases fragmented. In order to address the issue, it is important to first review where this shift has been implemented and which lessons can be learned from international experiences. The RCN is committed to working closely with its equivalent nursing organisations overseas to learn from international best practices and incorporate some of this learning to shape health and social care policy in the UK, and more specifically promote good nursing practice. This report will focus on system-wide or sector specific reforms in Australia, Canada, Sweden, Norway and Denmark as these countries have at one point or another addressed the need todeliver care outside of hospitals, either in patients' homes, GP clinics, community-basedcentres or care home settings.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.016 | 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".