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Record W2132208210 · doi:10.1258/smj.2011.011030

Do waiting list initiatives discriminate in favour of those in a higher socioeconomic group?

2011· article· en· W2132208210 on OpenAlexaff
Gavin Wood, C. R. Howie

Bibliographic record

VenueScottish Medical Journal · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineSocioeconomic statusGovernment (linguistics)Health carePopulationFirthFamily medicineEnvironmental healthEconomic growth

Abstract

fetched live from OpenAlex

The UK has a publicly funded health care system with open access to all. In the past, demand for services overwhelmed the resources available. Recent government initiatives have attempted to address this. To achieve shorter waiting times (and guaranteed waiting times), access to additional services has been purchased from the private sector under short-term initiatives, often at sites firth of the home health board. There has been a suspicion that patients from higher socioeconomic groups have benefited differentially from this by rapid access to private health care facilities, due to ease of transport. The aim of this study was to analyse whether a patient's socioeconomic group influenced their access to, and place of, surgery. Patients undergoing a primary total hip or knee arthroplasty in a single health region over a three-year period were identified and their social group was determined by postcode address. Analysis of 3888 patients operated on in four different treatment centres comparing the distribution of patients according to their social group, revealed no bias in the provision of treatment. The study group was comparable to the control population in that health region. In conclusion, the introduction of health policies to reduce time to orthopaedic treatment within one health board area has not resulted in patient bias.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.146
GPT teacher head0.318
Teacher spread0.172 · 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.

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
Published2011
Admission routes1
Has abstractyes

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