Improving adherence to multiple medications in older people in primary care: Selecting intervention components to address patient‐reported barriers and facilitators
Bibliographic record
Abstract
BACKGROUND: Medication adherence is vital to ensuring optimal patient outcomes, particularly amongst multimorbid older people prescribed multiple medications. Interventions targeting adherence often lack a theoretical underpinning and this may impact on effectiveness. The theoretical domains framework (TDF) of behaviour can aid intervention development by systematically identifying key determinants of medication adherence. OBJECTIVES: This study aimed to (i) identify determinants (barriers, facilitators) of adherence to multiple medications from older people's perspectives; (ii) identify key domains to target for behaviour change; and (iii) map key domains to intervention components [behaviour change techniques (BCTs)] that could be delivered in an intervention by community pharmacists. METHOD: Focus groups were conducted with older people (>65 years) receiving ≥4 medications. Questions explored the 12 domains of the TDF (eg "Knowledge," "Emotion"). Data were analysed using the framework method and content analysis. Identification of key domains and mapping to intervention components (BCTs) followed established methods. RESULTS: Seven focus groups were convened (50 participants). A wide range of determinants were identified as barriers (eg forgetfulness, prioritization of medications) and facilitators (eg social support, personalized routines) of adherence to multiple medications. Eight domains were identified as key targets for behaviour change (eg "Social influences," "Memory, attention and decision processes," "Motivation and goals") and mapped to 11 intervention components (BCTs) to include in an intervention [eg "Social support or encouragement (general)," "Self-monitoring of the behaviour," "Goal-setting (behaviour)"]. CONCLUSION: This study used a theoretical underpinning to identify potential intervention components (BCTs). Future work will incorporate the selected BCTs into an intervention that will undergo feasibility testing in community pharmacies.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".