Barriers to Optimal Control of Type 2 Diabetes in Malaysian Malay Patients
Bibliographic record
Abstract
There are a growing number of people diagnosed with diabetes. But, with the growing number of people diagnosed withdiabetes, Malaysia is not spared of this phenomenon, as prevalence stands at 14.9% of adult population. Adequate bloodglucose control is vital in diabetes management to prevent complications. Even so there is a lack of diabetic controlamong people with diabetes in Malaysia and we need to understand why this is. This study set out to explore theperspectives and experiences of Malay patients in managing Type 2 diabetes as a chronic illness and providerecommendations that aim to enhance adherence to treatment and help patients to improve their self-management skills.In-depth interviews were carried out on a purposeful sample of patients and their health care professional (HCPs).Interviews were recorded, transcribed and audiotapes were analysed using NVivo software to identify emerging themesand code according to categories. Interviews were conducted in an Endocrinology clinic in Malaysia with 18 Malaypatients (15-75 years, 9 males and 9 females) and 13 HCPs. Results indicated that themes that emerged from interviewswith the patients included problems with integrating the treatment regimen and difficulty developing coping skills toachieve the desired blood glucose level. Most patients lacked understanding of diabetes and management of diabetes,nature of diabetes, awareness of having diabetes, diabetic education, knowledge of diabetes, duration of illness,patients’ understanding of diabetes, physical effects of treatment, severity of symptoms and disease. Patients believedthat they needed to integrate many treatment requirements such as diet, medications, blood glucose monitoring andexercise into their daily routine. However, barriers to achieving good control of diabetes were found to be theconstraints in their ability to control diabetes. Education and knowledge related to diabetes that influencedunderstanding of the disease were also reasons for non-adherence to treatment regimen. Their beliefs and ability tominimise these barriers shaped their attitudes towards disease management. Patients were willing to discuss theirproblems about self-managing diabetes if some of these barriers were addressed during consultations. It can beconcluded that more positive approaches are needed in self-management of diabetes and health care professionalsinvolved in the management of diabetes need to understand their patients’ beliefs about their diabetes and constraintsfaced by their patients to promote more awareness and to foster greater control of diabetes and improve healthoutcomes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".