Improving appropriate polypharmacy for older people in primary care: selecting components of an evidence-based intervention to target prescribing and dispensing
Bibliographic record
Abstract
BACKGROUND: The use of multiple medicines (polypharmacy) is increasingly common in older people. Ensuring that patients receive the most appropriate combinations of medications (appropriate polypharmacy) is a significant challenge. The quality of evidence to support the effectiveness of interventions to improve appropriate polypharmacy is low. Systematic identification of mediators of behaviour change, using the Theoretical Domains Framework (TDF), provides a theoretically robust evidence base to inform intervention design. This study aimed to (1) identify key theoretical domains that were perceived to influence the prescribing and dispensing of appropriate polypharmacy to older patients by general practitioners (GPs) and community pharmacists, and (2) map domains to associated behaviour change techniques (BCTs) to include as components of an intervention to improve appropriate polypharmacy in older people in primary care. METHODS: Semi-structured interviews were conducted with members of each healthcare professional (HCP) group using tailored topic guides based on TDF version 1 (12 domains). Questions covering each domain explored HCPs' perceptions of barriers and facilitators to ensuring the prescribing and dispensing of appropriate polypharmacy to older people. Interviews were audio-recorded and transcribed verbatim. Data analysis involved the framework method and content analysis. Key domains were identified and mapped to BCTs based on established methods and discussion within the research team. RESULTS: Thirty HCPs were interviewed (15 GPs, 15 pharmacists). Eight key domains were identified, perceived to influence prescribing and dispensing of appropriate polypharmacy: 'Skills', 'Beliefs about capabilities', 'Beliefs about consequences', 'Environmental context and resources', 'Memory, attention and decision processes', 'Social/professional role and identity', 'Social influences' and 'Behavioural regulation'. Following mapping, four BCTs were selected for inclusion in an intervention for GPs or pharmacists: 'Action planning', 'Prompts/cues', 'Modelling or demonstrating of behaviour' and 'Salience of consequences'. An additional BCT ('Social support or encouragement') was selected for inclusion in a community pharmacy-based intervention in order to address barriers relating to interprofessional working that were encountered by pharmacists. CONCLUSIONS: Selected BCTs will be operationalised in a theory-based intervention to improve appropriate polypharmacy for older people, to be delivered in GP practice and community pharmacy settings. Future research will involve development and feasibility testing of this intervention.
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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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".