Hospitals forecast a rise in emergency admissions, while commissioners forecast a fall
Bibliographic record
Abstract
Cracks in NHS performance in England are appearing alongside signs of a “worrying mismatch” in planning between those who commission and those who provide services, an analysis shows. The health think tank the King’s Fund has said that growing pressure on finance and rising demand for services meant that hospitals had to choose between maintaining the quality of care and balancing the books. Many hospital trusts were recruiting more nurses, despite budgets being stretched to the limit: the nursing workforce rose by almost 9000 over the past six months to nearly 315 000, the “highest on record,” the fund said. The fund published its latest quarterly monitoring report, for the period from April to June 2014, on Thursday 17 July.1 It also reported that a quarter of NHS trust finance directors (18 of 73 surveyed) said that they expected to overspend their budgets in 2014-15. …
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.038 | 0.012 |
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".