A feasibility study of a theory-based intervention to improve appropriate polypharmacy for older people in primary care
Bibliographic record
Abstract
BACKGROUND: A general practitioner (GP)-targeted intervention aimed at improving the prescribing of appropriate polypharmacy for older people was previously developed using a systematic, theory-based approach based on the UK Medical Research Council's complex intervention framework. The primary intervention component comprised a video demonstration of a GP prescribing appropriate polypharmacy during a consultation with an older patient. The video was delivered to GPs online and included feedback emphasising the positive outcomes of performing the behaviour. As a complementary intervention component, patients were invited to scheduled medication review consultations with GPs. This study aimed to test the feasibility of the intervention and study procedures (recruitment, data collection). METHODS: GPs from two general practices were given access to the video, and reception staff scheduled consultations with older patients receiving polypharmacy (≥4 medicines). Primary feasibility study outcomes were the usability and acceptability of the intervention to GPs. Feedback was collected from GP and patient participants using structured questionnaires. Clinical data were also extracted from recruited patients' medical records (baseline and 1 month post-consultation). The feasibility of applying validated assessment of prescribing appropriateness (STOPP/START criteria, Medication Appropriateness Index) and medication regimen complexity (Medication Regimen Complexity Index) to these data was investigated. Data analysis was descriptive, providing an overview of participants' feedback and clinical assessment findings. RESULTS: Four GPs and ten patients were recruited across two practices. The intervention was considered usable and acceptable by GPs. Some reservations were expressed by GPs as to whether the video truly reflected resource and time pressures encountered in the general practice working environment. Patient feedback on the scheduled consultations was positive. Patients welcomed the opportunity to have their medications reviewed. Due to the short time to follow-up and a lack of detailed clinical information in patient records, it was not feasible to detect any prescribing changes or to apply the assessment tools to patients' clinical data. CONCLUSION: The findings will help to further refine the intervention and study procedures (including time to follow-up) which will be tested in a randomised pilot study that will inform the design of a definitive trial to evaluate the intervention's effectiveness. TRIAL REGISTRATION: ISRCTN18176245.
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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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".