Patients’ perspective of disease and medication adherence for type 2 diabetes in an urban area in Bangladesh: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Patients' perspective of diabetes and adherence to its prescribed medications is a significant predictor of glycemic control and overall management of the disease. However, there is a paucity of such information in Bangladesh. This study aimed to explore patients' perspective of diabetes, their experience of taking oral hypoglycemic medications and explore factors that contribute to medication adherence in patients with type 2 diabetes in Bangladesh. METHODS: We conducted in-depth face-to-face interviews with 12 type 2 diabetes patients attending a tertiary hospital in Dhaka city between February and March, 2014. Participants were purposively sampled representing different age groups, education levels, years since diagnosis with diabetes, and glycemic status, to achieve maximum variation sampling. All interviews were conducted using a topic guide and were audio-recorded, transcribed verbatim, checked for errors, coded and analyzed by means of a qualitative content analysis framework. RESULTS: The data analysis generated rich information on the participants' knowledge and perception on diabetes, its causes, self-management, medication use, adverse effects of medication use, medication adherence, and impact of diabetes, Although most of the participants demonstrated substantive knowledge on diabetes and its consequences, they also reported numerous misconceptions about the disease. Knowledge on diabetes medication, their appropriate use and side effects was rather poor. Respondents also reported non-compliance to dietary and physical activity advice by their physicians and concerns on diabetes diabetes-induced psychological stress. High cost of medications, concerns over medication side effects and forgetfulness was noted as factors for non-adherence to medication. CONCLUSION: Participants' knowledge and perception on diabetes are key factors determining their adherence to medications and, thereby, diabetes management. Healthcare providers should explore to better understand patients' perspective on diabetes, medication beliefs, identify psychological stress and provide more effective health education interventions to enhance medication adherence.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".