Bibliographic record
Abstract
A new report from the Health Foundation and Nuffield Trust suggests managers and policy makers are not able to base decisions about reforming health services on the best available evidence Improving quality and performance in the NHS requires a developmental approach that applies research to a planned process of change. Decision makers need many questions answering. How should clinical teams be organised and resourced to deliver higher quality, safer care? How could hospital environments be improved? How should local services be configured to ensure convenient access and optimal quality? And how can recruitment and retention of healthcare staff be enhanced? Despite clarity about the questions, decision makers feel they lack the research that would help them generate answers. So what can be done to improve the use of health service research? In 2002, the Health Foundation and the Nuffield Trust jointly commissioned a review of health services research in the United Kingdom.1 The aim was to examine how independent grant funders in health could enhance the contribution of health services research to improving services and policy making and to learn from the role of charitable foundations in other countries. Research for the review, conducted during January to August 2003, included interviews with 35 senior UK health services researchers, health service managers, policy makers, or research commissioners. It also included an analysis of case studies and a review of successful initiatives in the United States and Canada. The research showed that everyone involved with health services research is dissatisfied to some extent with the current research process, albeit from different perspectives (box 1). Improved hospital environments, such as this award-winning design (the new medical campus of the Norfolk and Norwich NHS Trust), are one result of the application of health services research ### Box 1: Perspectives on problem of health services research
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".