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Record W2167737863 · doi:10.1111/ecc.12171

Accessing cancer services in North West England: the Chinese population

2014· article· en· W2167737863 on OpenAlexaff
Alicia‐Marie Conway, A.R. Clamp, J. Hasan, D. Goonetilleke, Kelsey Shore, L.M.J. Wong, Joann M. Wong, Gordon C. Jayson

Bibliographic record

VenueEuropean Journal of Cancer Care · 2014
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsHealth Sciences Centre
Fundersnot available
KeywordsMedicineFamily medicinePopulationCancerService (business)Health careNursingEnvironmental healthEconomic growth

Abstract

fetched live from OpenAlex

Relatively few Chinese patients access tertiary cancer services in North West England. We investigated the reasons behind this using a culturally sensitive questionnaire. The questionnaire, completed by 214 Chinese people in English, Cantonese or Mandarin, evaluated the Chinese population's access and satisfaction with primary care, understanding of cancer and awareness of local cancer services. Ninety-five per cent of respondents were registered with a general practitioner (GP) and 75% had accessed primary care in the last year. Satisfaction with GP consultations was high but a third of respondents reported a lack of confidence in local National Health Service (NHS) services. Only 57% of eligible women had attended cervical screening programmes. The overall understanding of the causes and treatment of cancer and cancer services in the North West was poor. Despite registration with primary healthcare, the Chinese population under-utilise cancer prevention programmes and tertiary cancer services because of a lack of awareness and understanding of cancer services in the North West. A significant proportion of the population is dissatisfied with the perceived slow service and lack confidence in services, with 41% considering using healthcare abroad. These data highlight the critical need to engage with, educate and support the Chinese population if they are to access NHS cancer services.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.026
GPT teacher head0.334
Teacher spread0.307 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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