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Record W2988203597 · doi:10.12968/bjhc.2019.0016

Austerity in the UK and poor health: were deaths directly affected?

2019· article· en· W2988203597 on OpenAlexaboutno aff
Rod Jones

Bibliographic record

VenueBritish Journal of Healthcare Management · 2019
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
Fundersnot available
KeywordsAusterityQuarter (Canadian coin)Government (linguistics)Mortality rateOlder peopleDemographyMedicineDemographic economicsPolitical scienceGerontologyEconomicsGeographySociologyPoliticsLaw

Abstract

fetched live from OpenAlex

A dramatic increase in deaths in the UK since 2011 has defied actuarial forecasts. This has led some to propose a direct link between the rise in deaths and government social care austerity. However, several facts argue against this link. Firstly, age standardised mortality in the second quarter of 2019 was statistically lower than the second quarter in all years since 2001, clearly austerity is still present, but age standardised mortality has recovered. Also, deaths have increased equally across the whole of the UK, whereas social care austerity has largely been restricted to England. English citizens who are residents outside of the UK also show the same trend. These effects are highly reminiscent of a recurring series of disease outbreaks of an unidentified pathogen. In addition, increases in deaths are always linked to increases in hospital admissions. This link arises since around half of a person's lifetime use of acute services is compressed into the last 6 months of life, irrespective of the age at death. This is called the nearness-to-death effect. International research is needed to understand exactly why deaths are behaving in this unique way. While austerity has created a significant problem relating to delayed discharges in hospitals and has highlighted serious problems with how end-of-life care is to be funded, it seemingly cannot be blamed for the increased mortality rate.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.391
Teacher spread0.360 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2019
Admission routes1
Has abstractyes

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