Fixing the broken image of care homes, could a ‘care home innovation centre’ be the answer?
Bibliographic record
Abstract
The UK has many excellent care homes that provide high-quality care for their residents; however, across the care home sector, there is a significant need for improvement. Even though the majority of care homes receive a rating of 'good' from regulators, still significant numbers are identified as requiring 'improvement' or are 'inadequate'. Such findings resonate with the public perceptions of long-term care as a negative choice, to be avoided wherever possible-as well as impacting on the career choices of health and social care students. Projections of current demographics highlight that, within 10 years, the part of our population that will be growing the fastest will be those people older than 80 years old with the suggestion that spending on long-term care provision needs to rise from 0.6% of our Gross Domestic Product in 2002 to 0.96% by 2031. Teaching/research-based care homes have been developed in the USA, Canada, Norway, the Netherlands and Australia in response to scandals about care, and the shortage of trained geriatric healthcare staff. There is increasing evidence that such facilities help to reduce inappropriate hospital admissions, increase staff competency and bring increased enthusiasm about working in care homes and improve the quality of care. Is this something that the UK should think of developing? This commentary details the core goals of a Care Home Innovation Centre for training and research as a radical vision to change the culture and image of care homes, and help address this huge public health issue we face.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.065 | 0.052 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".