A consultation-level intervention to improve care of frequently attending patients: a cluster randomised controlled feasibility trial
Bibliographic record
Abstract
Background Frequent attenders (FAs) to primary care receive considerable NHS resources without necessarily gaining benefit, and may even be harmed. Aim To assess the feasibility of a consultation-level intervention to improve care and address service use of FAs. Design & setting A cluster randomised controlled feasibility trial was undertaken. The study used a mixed-methods process evaluation and took place in six practices in England. Method All practices screened the top 3% of all attending patients over the previous 12 months for eligibility. Following randomisation, intervention patients were matched with named GPs, trained to use the Background, Affect, Trouble, Handling, Empathy (BATHE) technique during consultations. Telephone consultations were encouraged. Feasibility outcomes assessed were recruitment, retention, data collection and completeness, implementation fidelity, and acceptability Results A total of 599/1328 (45.1%) FAs were eligible. Four practices were randomised to the intervention (n = 451) and two to usual care (n = 148). A total of 96 (23.7%) patients were recruited to complete questionnaires. Retention and completeness of data were good; for example, 76% of those agreeing to complete questionnaires did so at the 12-month assessment point. Thirty-four GPs were trained and delivered BATHE ≥1 times to 50.1% of patients (n = 577 consultations). There were minimal increases in continuity and telephone consultations. Patients were positive about the intervention, but noticed little change in their care. Despite valuing BATHE, low adherence to training was indicated and GPs used it less than anticipated. Conclusion It was feasible to identify FAs and collect trial data. GPs were keen to engage and there was evidence that the BATHE technique was taken into practice. Optimising training is likely to improve fidelity. The intervention was low cost and low risk.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".