The Big-Five personality traits and quality of life in elderly Malaysian patients with diabetes mellitus: A cross-sectional study
Bibliographic record
Abstract
Abstract Background: Diabetes mellitus is highly prevalent in the elderly population, with a significant impact on quality of life. This study aimed to explore the relationship between personality traits and quality of life in an elderly population with diabetes. Methods: A cross-sectional study was conducted at the Universiti Kebangsaan Malaysia Medical Center. Outpatients above 60 years old with a diagnosis of type 1 or type 2 diabetes mellitus were recruited. Sociodemographic and clinical information were obtained. Quality of life was assessed using the WHO Quality of Life-BREF questionnaire and personality traits were assessed using the Big Five Inventory questionnaire. Depression and anxiety were measured with Beck Depression Inventory and Generalized Anxiety Disorder 7-item scale respectively and controlled for in statistical analyses. Results: There were 170 study participants (median age=69.0 years; IQR: 65.0-73.0; 51.2% male). In stepwise linear regression models, higher conscientiousness scores (β=0.156; p=0.044) and lower neuroticism scores (β=-0.176; p=0.028) were associated with greater quality of life in the physical health domain. Higher extraversion scores (β=0.209; p=0.001) and higher conscientiousness scores (β=0.248; p<0.001) were associated with greater quality of life in the psychological health domain. Higher agreeableness scores (β=0.286; p<0.001) were associated with greater quality of life in the social relationship domain. Finally, higher agreeableness scores (β=0.327; p<0.001) and lower neuroticism scores (β=-0.223; p=0.001) were associated with greater quality of life in the environment domain. Conclusions: Personality traits were closely associated with all domains of quality of life among elderly patients with diabetes mellitus. Premorbid personality may have important role in moderating the impact of diabetes mellitus on the lives of elderly patients.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".