Is there any relationship between medication compliance and affective temperaments in patients with type 2 diabetes?
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes mellitus (DM) is the most common type of diabetes.The number of patients with this disease is expected torise in future. Given the increasing prevalence of diabetes, there is an urgent need for the treatment of diabetes and the associated complications. Glycemic control largely depends on compliance with medication therapies. In fact, the most common problem in patients with diabetes is lack of medication compliance. This study aimed to determine the relationship between affectivetemperaments and medication compliance in patients with type 2 diabetes. METHODS: In this cross-sectional research, the study population consisted of all patients referring to the endocrinology clinic of Ayatollah Taleghani Hospital of Tehran in 2010 and 2011. Two hundreds and seven patients were selected, using available sampling method. In this study, we used Temperament Evaluation of Memphis, Pisa, Paris, and San Diego Auto questionnaire (TEMPS-A), a single-item scale of medication compliance, Beck Depression Inventory-II (BDI-II), and a researcher-made questionnaire to assess the patients' demographic information. All participants completed the questionnaires related to affective temperaments, medication compliance, depression and demographic information. The obtained data were recorded on the prepared sheets. RESULTS: Of 207 patients, 79 (38.2%) and 128 (61.8%) subjects were male and female, respectively. The mean and standard deviation of demographic data were calculated. In total, 13.5%, 19.3%, and 8.2%of the participants had mild, moderate, and severe depression, respectively. In this study, as the single-item rating scale indicated, medication compliance and non-compliance were reported in 75.4% and 24.6% of the patients, respectively. Among the demographic characteristics and clinical variables, frequency of patient referral and glycated hemoglobin level were predictors of medication compliance. Also, among affective temperaments, irritable temperament was a predictor of medication compliance. CONCLUSIONS: The obtained findings emphasize the importance of psychological factors such as personality characteristics in medication compliance of patients with diabetes. In case a patient obtains high scores in irritable temperament (which indicate poor medication compliance), he/she should follow special training programs to improve his/her medication compliance.
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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 | 0.001 | 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 | 0.000 |
| 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 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".