Improving the delivery of care for patients with diabetes through understanding optimised team work and organisation in primary care
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes is an increasingly prevalent chronic illness and is an important cause of avoidable mortality. Patients are managed by the integrated activities of clinical and non-clinical members of the primary care team. Studies of the quality of care for patients with diabetes suggest less than optimum care in a number of areas. AIM: The aim of this study is to improve the quality of care for patients with diabetes cared for in primary care in the UK by identifying individual, team, and organisational factors that predict the implementation of best practice. DESIGN: Participants will be clinical and non-clinical staff within 100 general practices sampled from practices who are members of the MRC General Practice Research Framework. Self-completion questionnaires will be developed to measure the attributes of individual health care professionals, primary care teams (including both clinical and non-clinical staff), and their organisation in primary care. Questionnaires will be administered using postal survey methods. A range of validated theories will be used as a framework for the questionnaire instruments. Data relating to a range of dimensions of the organisational structure of primary care will be collected via a telephone interview at each practice using a structured interview schedule. We will also collect data relating to the processes of care, markers of biochemical control, and relevant indicator scores from the quality and outcomes framework (QOF). Process data (as a proxy indicator of clinical behaviours) will be collected from practice databases and via a postal questionnaire survey of a random selection of patients from each practice. Levels of biochemical control will be extracted from practice databases. A series of analyses will be conducted to relate the individual, team, and organisational data to the process, control, and QOF data to identify configurations associated with high quality care. STUDY REGISTRATION: UKCRN ref:DRN120 (ICPD).
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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 | 0.008 | 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 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".