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Record W2144168541 · doi:10.1046/j.1464-5491.19.s4.4.x

The GP perspective: problems experienced in providing diabetes care in UK general practice

2002· article· en· W2144168541 on OpenAlexaff
Gina Agarwal, M. Pierce, Deborah Ridout

Bibliographic record

VenueDiabetic Medicine · 2002
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicinePrimary careFamily medicineNursingHealth careGeneral practiceMEDLINE

Abstract

fetched live from OpenAlex

AIMS: To describe the problems and barriers perceived by general practitioners (GPs) whilst providing diabetes care in primary care in England and Wales and to identify those health authorities (HAs) in which primary care reported the most and least difficulty. DESIGN: Descriptive postal survey using a self-administered questionnaire. SUBJECTS: One thousand eight hundred and seventy-three randomly sampled GP practices (one in five practices). RESULTS: One thousand three hundred and twenty (70%) responded. Getting patients to alter their lifestyles was perceived as causing the most difficulty in managing individual patients, followed by lack of time, patients' nonattendance, noncompliance with medical regimens and poor communication with secondary care. The greatest barriers to practices providing desirable care were lack of time/under-funding and keeping up to date in the area of diabetes, followed by lack of space, inadequate chiropody, dietetics, ophthalmology and access to secondary care. There are important differences between HAs in the difficulties experienced by primary care teams and we have ranked the HAs accordingly. CONCLUSION: The study has identified what problems need tackling in order to assist primary care to deliver good quality diabetes care, and has highlighted HAs where primary care needs further help, and HAs where examples of good practice may be found and useful lessons learnt. This should form the basis of a needs-based research and development programme for diabetes in primary care.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.050
GPT teacher head0.415
Teacher spread0.366 · 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 designQualitative
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

Citations21
Published2002
Admission routes1
Has abstractyes

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