The role of affective temperaments in self-care and medication adherence among individuals with bipolar disorder: a moderation analysis
Bibliographic record
Abstract
Background: Affective temperament, defined as the fundamental predisposition from which normal affective states originate or as the constitutional core of personality, play a crucial role in mood disorders, particularly bipolar disorders. Understanding the relationship between temperaments, treatment adherence, and self-care is crucial for effective management and improved clinical results. Objectives: This study aims to (1) assess the correlation between affective temperaments and treatment adherence, (2) investigate the relationship between affective temperaments and self-care abilities, (3) identify predictors of treatment adherence, and (4) explore the moderating effect of self-care on the relationship between treatment adherence and depressive temperament in individuals with bipolar disorder. Methods: A cross-sectional study was conducted with 231 individuals diagnosed with bipolar disorder (BD) type I (N=160) and type II (N=71). The participants were evaluated using the following psychometric tools: Temperament Evaluation of Memphis, Pisa, and San Diego (TEMPS) to assess affective temperaments, Personal and Social Performance Scale (PSP) to evaluate social functioning and self-care abilities, and Morisky Medication Adherence Scale (MMAS) to measure treatment adherence. The study involved statistical analyses to examine correlations, identify predictors, and explore moderating effects. Results: The findings revealed significant correlations between affective temperaments and both treatment adherence and self-care abilities. Specifically, hyperthymic temperament was positively associated with higher treatment adherence, whereas cyclothymic and depressive temperaments were linked to lower adherence. Self-care abilities were found to mediate the relationship between depressive temperament and treatment adherence, suggesting that improved self-care can enhance adherence in individuals with depressive temperament. Conclusions: Affective temperaments significantly influence treatment adherence and self-care abilities in individuals with bipolar disorder. The mediating role of self-care highlights the importance of developing targeted interventions to improve self-care practices, thereby enhancing treatment adherence and overall well-being. Personalized treatment strategies based on temperament assessments could lead to better clinical outcomes and quality of life for individuals with bipolar disorder.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 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".