Four parts or one whole: The National Health Service (NHS) post-devolution
Bibliographic record
Abstract
AIM(S): There is a need for nurse and midwifery managers to have an understanding of devolution and its implications for them and their colleagues. This paper will explain devolution, consider some health and social care policy including similarities and differences, and assess the impact of devolution on the nursing workforce and the regulation of nursing across the four countries of the United Kingdom (UK). BACKGROUND: If managers are to manage effectively it is critical that they remain aware of emerging policy development and outcomes across the UK. It is now more important than ever that nurses maintain a keen eye on the impact divergent policy is having on practice as well as the UK nursing workforce. EVALUATION: The impact of devolution across the UK will be explored using convergence and divergence as a framework; commencing by providing an overview of devolution and health, moving on to examine health policy in action across the four countries. KEY ISSUES: Healthcare is highly political in nature. Devolution has implications for all, and adds to the complexity of health and social care provision. If managers are to manage effectively it is critical that they remain aware of emerging policy development and outcomes across the UK. CONCLUSION: It is equally important that nurses, and nurse managers, develop and draw upon their political leadership skills, actively engaging in policy debates to ensure that when policies are translated into practice their outcomes are optimal in terms of quality, efficiency and sustainability. Implications for nursing management There is a need for nurse and midwifery managers to have an understanding of post-devolution structures and how they operate in order to work effectively, as well as to learn from the experiences of other parts of the UK.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".