Two thirds of NHS trusts forecast a deficit this year, up from a quarter last year
Bibliographic record
Abstract
Two thirds of NHS trusts in England, including nine in 10 acute care trusts, are forecasting a deficit at the end of the 2015-16 financial year, a survey of finance directors by the think tank the King’s Fund shows. The survey of 254 finance directors at NHS trusts was carried out between 5 June and 22 June 2015. One hundred responded, of whom 66% forecast an end of year deficit, up from 25% at the same time last year. Among acute care trusts 89% forecast a deficit, up from 21% last year. And 62% of the finance directors said that even these pessimistic forecasts depended on the provision of additional financial support, running down their reserves, or both. Richard Murray, director of policy at the King’s Fund, said, “Last year’s deficit among NHS providers was unprecedented, but this year is shaping up to be much worse. …
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".