MétaCan
Menu
Back to cohort
Record W1781258054 · doi:10.1136/bmj.h3670

Two thirds of NHS trusts forecast a deficit this year, up from a quarter last year

2015· article· en· W1781258054 on OpenAlexaboutno aff
Ingrid Torjesen

Bibliographic record

VenueBMJ · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth Services Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)PessimismFinanceEconomicsBusinessHistory

Abstract

fetched live from OpenAlex

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. …

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.008

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.

Opus teacher head0.129
GPT teacher head0.461
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueBMJSame topicHealth Services Management and PolicyFrench-language works237,207