Guest editorial: Integrated services for older people – the key to unlock our health and care services and improve the quality of care?
Bibliographic record
Abstract
Across the United Kingdom, there is growing interest from politicians, national and local service leaders and clinicians in integration, care co-ordination and partnership working (Curry and Ham, 2010; Goodwin et al., 2013b; Ham et al., 2011, 2013; NHS Confederation and Royal College of General Practitioners, 2013). Some specific policy levers and targeted funds and financial instruments have aimed to facilitate integration and make it the norm rather than the exception (Bennett and Humphries, 2013; Royal College of Nursing, 2014). There is a tacit assumption that a shift towards these models will be ‘‘win/win’’ – improving care for individual service users, whilst also saving services from the ‘‘triple threat’’ of population demographics, rising demand and financial austerity (Naylor et al., 2013). Influential health think tanks the King’s Fund and Nuffield Trust (Goodwin et al., 2013a) and professional Bodies such as the Royal Colleges of Physicians (RCP, 2013), General Practitioners (RCGP, 2014) and Nursing (RCN, 2014) have focused increasing efforts on the cause of integration. Whilst my editorial focuses on the UK, other health systems facing similar challenges are increasingly embracing the same agenda (Goodwin et al., 2013a, 2013b, 2014; Ham 2011, Timmins and Ham, 2013). I won’t get drawn into endless, abstruse definitions of what we mean by integrated services. There is a body of literature for those with a niche interest in terminology or who like to close down discussion by saying ‘‘everyone’s talking about it, but no-one can agree what it is’’. I am more concerned by how integration might help support people in need of health and social care and improve their experience and outcomes. We can return to definitions later. Let’s start ‘‘bottom up’’ with people before worrying ‘‘top down’’ about structure and process.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.017 | 0.011 |
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".