Identifying determinants of medication adherence following myocardial infarction using the Theoretical Domains Framework and the Health Action Process Approach
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Despite evidence-based recommendations, adherence with secondary prevention medications post-myocardial infarction (MI) remains low. Taking medication requires behaviour change, and using behavioural theories to identify what factors determine adherence could help to develop novel adherence interventions. OBJECTIVE: Compare the utility of different behaviour theory-based approaches for identifying modifiable determinants of medication adherence post-MI that could be targeted by interventions. METHODS: Two studies were conducted with patients 0-2, 3-12, 13-24 or 25-36 weeks post-MI. Study 1: 24 patients were interviewed about barriers and facilitators to medication adherence. Interviews were conducted and coded using the Theoretical Domains Framework. Study 2: 201 patients answered a telephone questionnaire assessing Health Action Process Approach constructs to predict intention and medication adherence (MMAS-8). RESULTS: Study 1: domains identified: Beliefs about Consequences, Memory/Attention/Decision Processes, Behavioural Regulation, Social Influences and Social Identity. Study 2: 64, 59, 42 and 58% reported high adherence at 0-2, 3-12, 13-24 and 25-36 weeks. Social Support and Action Planning predicted adherence at all time points, though the relationship between Action Planning and adherence decreased over time. CONCLUSIONS: Using two behaviour theory-based approaches provided complimentary findings and identified modifiable factors that could be targeted to help translate Intention into action to improve medication adherence post-MI.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it