Sociodemographic factors associated with knowledge of type 2 diabetes in rural Tamil Nadu, India
Bibliographic record
Abstract
INTRODUCTION: This study aimed to investigate awareness of type 2 diabetes and how sociodemographic factors influence diabetes knowledge in a rural population of Tamil Nadu, India. Previous research has identified poor awareness of diabetes in several low and middle-income countries, which can lead to a high prevalence of undiagnosed diabetes. India having the second highest prevalence of diabetes globally, it is increasingly important to assess how diabetes can be addressed in rural Indian populations. METHODS: Systematic random sampling was used to gather study participants in 17 villages within the Krishnagiri district of Tamil Nadu, India. Data on diabetes knowledge was collected using a validated questionnaire. Knowledge score range was 0-8; a score of zero was designated as 'low knowledge', scores 1-4 as 'moderate knowledge', and scores 5-8 as 'good knowledge'. Associations between sociodemographic factors and composite diabetes knowledge score were assessed using a multinomial logistic GLLAMM model in Stata. RESULTS: A total of 753 individuals participated in the study. The average age of participants was 47 years and 55% were women. Overall awareness of diabetes was low, with 66% of individuals having no knowledge of diabetes. Only 16% and 17% achieved a moderate and a good knowledge score, respectively. Achieving a moderate knowledge score was significantly positively associated with education, wealth, participation in the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), and business ownership as a source of income. Achieving a good knowledge score was significantly positively associated with education, wealth, rurality, participation in MGNREGA, business ownership as a source of income, and frequency of healthcare utilization. Rurality was significantly negatively associated (relative risk ratio (95% confidence interval)) with both moderate knowledge score (0.34 (0.19-0.59)), and good knowledge score (0.43 (0.24-0.74)). The strongest predictor of having a good knowledge score was having a high-school graduate or post-secondary education (11.07 (4.44-27.61)). Enrolment in MGNREGA employment was the strongest predictor for having a moderate knowledge score (3.27 (1.93-5.54)), as well as strongly associated with having a good knowledge score (2.39 (1.31-4.36)). CONCLUSION: The low awareness of diabetes among participants of this study raises serious concerns for public health in India. Public health efforts must prioritize health equity to lessen the impacts of diabetes in rural populations, where individuals face systemic barriers to receiving prevention and treatment for conditions such as diabetes.
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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) | 0.001 | 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".