Exploring behaviour change in general practice consultations: A realist approach
Bibliographic record
Abstract
OBJECTIVES: While general practice involves supporting patients to modify their behaviour, General Practitioners (GPs) vary in their approach to behaviour change during consultations. We aimed to identify mechanisms supporting GPs to undertake successful behaviour change in consultations for people with T2DM by exploring (a) the role of GPs in behaviour change, (b) what happens in GP consultations that supports or impedes behaviour change and (c) how context moderates the behaviour change consultation. METHODS: = 16) across Australia. Data were analysed thematically using a realist evaluation approach. RESULTS: Perspectives about the role of GPs were highly variable, ranging from the provision of test results and information to a relational approach towards shared goals. A GP-patient relationship that includes collaboration, continuity and patient-driven care may contribute to a sense of successful change. Different patient and GP characteristics were perceived to moderate the effectiveness and experience of behaviour change consultations. DISCUSSION: When patient factors are recognised in consultations, a relational approach becomes possible and priorities around behaviour change, that might be missed in a transactional approach, can be identified. Therefore, GP skills for engaging patients are linked to a person-centred approach.
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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 | 0.046 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".