The 5-Factor Model of Personality and Antidepressant Medication Compliance
Bibliographic record
Abstract
OBJECTIVE: Medication noncompliance is a significant problem for effective pharmacologic treatment of major depressive disorder (MDD). Attempts to explore predictors of compliance have primarily focused on demographic characteristics; for the most part, these have been shown to be unrelated to compliance. Conversely, the relation between personality characteristics and compliance has been relatively understudied. The primary purpose of this study was to explore the relation between personality characteristics and compliance with antidepressant medication in patients with major depressive disorder (MDD). METHOD: Over 14 weeks, we evaluated a sample of outpatients (n = 65) who were receiving antidepressant treatment. We monitored compliance electronically, using the Medication Event Monitoring System. We assessed personality characteristics with the NEO Five-Factor Inventory-Revised. We also assessed depression severity and the frequency and severity of side effects. RESULTS: Extraversion was a significant negative predictor of compliance. This was largely explained by the relation between compliance and the Activity facet within Extraversion. We also found a negative relation between the Feelings facet and compliance, while the Modesty facet was a significant positive predictor of compliance with antidepressant medication. Neither severity of depression nor side effects predicted compliance. CONCLUSIONS: These results suggest that correlates of personality are important, although frequently ignored, predictors of compliance with antidepressant medication. Identifying predictors of medication compliance may help in the development of individualized treatment regimens and lead to improved therapeutic outcome in the treatment of MDD.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".