Personality Makes a Difference: Attachment Orientation Moderates Theory of Planned Behavior Prediction of Cardiac Medication Adherence
Bibliographic record
Abstract
OBJECTIVE: To achieve a comprehensive understanding of patients' adherence to medication following acute coronary syndrome (ACS), we assessed the possible moderating role played by attachment orientation on the effects of attitudes, subjective norms, and perceived behavioral control (PBC), as derived from the Theory of Planned Behavior (TPB; Ajzen, 1991), on intention and reported adherence. METHOD: A prospective longitudinal design was employed. During hospitalization, ACS male patients (N = 106) completed a set of self-report questionnaires including sociodemographic variables, attachment orientation, and measures of TPB constructs. Six months post-discharge, 90 participants completed a questionnaire measuring adherence to medication. RESULTS: Attachment orientations moderated some of the predictions of the TPB model. PBC predicted intention and reported adherence, but these associations were found to be significant only among individuals with lower, as opposed to higher, attachment anxiety. The association between attitudes and intention was stronger among individuals with higher, as opposed to lower, attachment anxiety. Only among individuals with higher attachment avoidance, subjective norms were negatively associated with intention to take medication. CONCLUSIONS: Cognitive variables appear to explain both adherence intention and behavior, but differently, depending on individuals' attachment orientations. Integrating personality and cognitive models may prove effective in understanding patients' health behaviors.
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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 | 0.002 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".