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Record W2007551914 · doi:10.1136/bmj.c6024

Where do the cuts leave the NHS?

2010· article· en· W2007551914 on OpenAlexaboutno aff
Nick Timmins

Bibliographic record

VenueBMJ · 2010
Typearticle
Languageen
FieldHealth Professions
TopicHealth Services Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Service (business)Health careBusinessLabour economicsPublic administrationPolitical scienceEconomicsMarketingEconomic growth

Abstract

fetched live from OpenAlex

The UK’s coalition government has honoured its promise to give the NHS an increase in real terms. But the rise—0.1% a year until 2014—could not have been smaller, reports Nick Timmins, and will leave the service struggling to meet rising demand, while cuts elsewhere could leave healthcare workers to pick up the pieces

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.062
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.061
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.062
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0090.011
Scholarly communication0.0220.026
Open science0.0030.006
Research integrity0.0290.029
Insufficient payload (model declined to judge)0.0610.015

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 head0.072
GPT teacher head0.493
Teacher spread0.421 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations7
Published2010
Admission routes1
Has abstractyes

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