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Record W3114470647 · doi:10.1093/pubmed/fdaa207

Charging of overseas visitors in England and universal health coverage: a cross-sectional analysis of NHS trusts

2020· article· en· W3114470647 on OpenAlexfundno aff
Joanna Dobbin, Adrienne Milner, Alexander Dobbin, Jessica Potter

Bibliographic record

VenueJournal of Public Health · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Healthcare and Medical Tourism
Canadian institutionsnot available
FundersMedical Research CouncilMedical Research Council Canada
KeywordsCross-sectional studyPublic healthEnvironmental healthMedicineOptometryFamily medicineNursingPathology

Abstract

fetched live from OpenAlex

BACKGROUND: In 2017, new regulations in England introduced upfront charging for non-urgent care within the National Health Service (NHS). Individuals from outside the European Economic Area who have not paid the immigration surcharge are chargeable for NHS care at 150% of cost. METHODS: A freedom of information (FOI) request was sent to 135 acute non-specialist NHS trusts in England to create a database of overseas visitors charges. This was analysed using multiple linear regression to explore the relationship between sex, age, nationality, ethnicity, urgency and the cost of healthcare. RESULTS: Of 135 acute non-specialist trusts in England 64 replied, providing a data set of 13 484 patients. Women were found to be invoiced higher amounts than men (P = 0.002). Patients were more likely to be women (63 versus 37% men), and within this group, almost half of patients were of reproductive age, with 47.9% (3165) aged 16-40 years old. Only seven trusts supplied data on urgency, and within these trusts the urgency of treatment was significantly related to cost, with the most urgent (immediately necessary) treatment costing the most (P < 0.001). CONCLUSION: This research reflects that that migrant women, and particularly undocumented women, are disproportionately impacted by the NHS charging policies in England.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.115
GPT teacher head0.456
Teacher spread0.341 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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