Process evaluation of the SPPiRE trial: a GP delivered medication review of polypharmacy, deprescribing and patient priorities in older people with multimorbidity
Bibliographic record
Abstract
Background: The SPPiRE cluster randomised controlled trial (RCT) found that a GP delivered medication review that incorporated screening potentially inappropriate prescriptions (PIP), a brown bag review and a patient priority assessment, resulted in a significant but small reduction in the number of medicines and no significant reduction in PIP. Objective: To explore the experiences of GPs and patients engaged in the SPPiRE intervention and the potential for system wide implementation. Design: Mixed methods process evaluation; quantitative data was collected from the SPPiRE intervention website and qualitative data via semi-structured interviews. Setting and participants: 51 general practices throughout Ireland, and 404 participants with multimorbidity aged ≥65 years, prescribed ≥15 medicines participated in the RCT. Qualitative data was collected with purposive samples of intervention GPs (18/26) and patients (27/208). Methods: Quantitative data was analysed descriptively, qualitative data thematically and both were integrated using a triangulation protocol. Results: The analysis generated three themes, intervention implementation, mechanisms of action, and both were underpinned by the theme of context. One fifth of patients had no review, primarily due to insufficient GP time. The brown bag review component resulted in the most medication changes, particularly stopping a medicine. GPs felt it easier to change medicines if the patient was well known to them, and patients were generally receptive to change. GPs identified lack of integration into practice software systems and resources as barriers to future implementation. Conclusion: Consideration of implementation of successful interventions is key to informing policy and integration into clinical practice. GPs and patients viewed the intervention positively, but implementation will depend on resourcing and integration into practice software systems. Trial registration number: ISRCTN12752680
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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 | 0.055 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".