Illness Perception and Quality of Life in Type 2 Diabetes Mellitus Patients in Lampung, Indonesia
Bibliographic record
Abstract
AIMS: The aim of this study was to determine the correlation between illness perception and QoL in Type 2 Diabetes Mellitus (T2DM) patients.MATERIAL & METHODS: We used cross-sectional design. The subjects were recruited from the Pringsewu Government Hospital in Lampung, Indonesia, and underwent T2DM treatment from May-July 2016. The subjects have met the inclusion and exclusion criteria. The inclusion criteria were patients age 15-65 with a diagnosis of T2DM with complications for more than 3 months prior and who consented to participate in the study. Participants used the self-reported questionnaire BIPQ (Brief Illness Perception Questionnaire) to measure illness perception and the SF-36 (Short Form-36) questionnaire to measure QoL. Statistical analysis used in this study were Pearson correlation and multivariate linear regression to test between illness perception and quality of life (QoL) domains. The correlation between variable were statistically significant if p value < 0.05.RESULTS: The domain of treatment management had the highest score among all BIPQ domains (mean: 8.55; SD: 1.99). Emotional well-being had the highest scores among the SF-36 domains (mean: 72.69; SD: 17.33). The energy domain in QoL was significantly predicted by consequence, personal management, and identity in the BIPQ illness perception components (p <0.0001). Moreover, the role limitation component was significantly predicted by emotional response, coherence and random blood glucose levels (p <0.0001).CONCLUSIONS: This study significantly showed weak positive correlations between illness perception and QoL in T2DM patients. An education strategy aimed at changing these negative emotional responses to improve patients’ role limitations due to emotional function should be considered.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".