Development of a complex community pharmacy intervention package using theory-based behaviour change techniques to improve older adults’ medication adherence
Bibliographic record
Abstract
BACKGROUND: To improve the effectiveness of interventions targeting non-adherence in older adults, a systematic approach to intervention design is required. The content of complex interventions and design decisions are often poorly described in published reports which makes it difficult to explore why they are ineffective. This intervention development study reports on the design of a community pharmacy-based adherence intervention using 11 Behaviour Change Techniques (BCTs) which were identified from previous qualitative research with older patients using the Theoretical Domains Framework. METHODS: Using a group consensus approach, a five-step design process was employed. This focused on decisions regarding: (1) the overall delivery format, (2) formats for delivering each BCT; (3) methods for tailoring BCTs to individual patients; (4) intervention structure; and (5) materials to support intervention delivery. The APEASE (Affordability; Practicability; Effectiveness/cost-effectiveness; Acceptability; Side effects/safety; Equity) criteria guided the selection of BCT delivery formats. RESULTS: Formats for delivering the 11 BCTs were agreed upon, for example, a paper medicines diary was selected to deliver the BCT 'Self-monitoring of behaviour'. To help tailor the intervention, BCTs were categorised into 'Core' and 'Optional' BCTs. For example, 'Feedback on behaviour' and 'Action planning' were selected as 'Core' BCTs (delivered to all patients), whereas 'Prompts and cues' and 'Health consequences' were selected as 'Optional' BCTs. A paper-based adherence assessment tool was designed to guide intervention tailoring by mapping from identified adherence problems to BCTs. The intervention was designed for delivery over three appointments in the pharmacy including an adherence assessment at Appointment 1 and BCT delivery at Appointments 2 and 3. CONCLUSIONS: This paper details key decision-making processes involved in moving from a list of BCTs through to a complex intervention package which aims to improve older patients' medication adherence. A novel approach to tailoring the content of a complex adherence intervention using 'Core' and 'Optional' BCT categories is also presented. The intervention is now ready for testing in a feasibility study with community pharmacists and patients to refine the content. It is hoped that this detailed report of the intervention content/design process will allow others to better interpret the future findings of this work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".