The GP perspective: problems experienced in providing diabetes care in UK general practice
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".