Predictors of Awareness and Management Practices of Diabetes among Rural Dwellers of Sindh
Bibliographic record
Abstract
INTRODUCTION: The most substantial method of control for the spread of DM, is the spreading of knowledge and information regarding DM and its complications. Therefore, the objective of this study was to investigate and evaluate the level of knowledge, awareness and management practices among people suffering from DM in the rural areas of Sindh. METHODOLOGY: A descriptive cross sectional survey was performed on 400 Diabetes Mellitus (DM) Type 2 patients from March 2015 to June 2015, dwelling in rural areas of Sindh province, South Pakistan. A paper based questionnaire was used to determine sociodemographic features, knowledge and awareness with regards to DM and its complications and last part evaluated management practices to manage diabetes. RESULTS: Only 50% participants knew that DM is a condition of high blood glucose and only 39% considered it as a preventable disease. With regards to management practices, only 65.0% had a home glucometer and 48% regularly checked their blood sugar levels. Family history of DM, BMI, education level, monthly household income, marital status and age were important predictors of knowledge among rural dwellers. CONCLUSION: Our study has revealed lack of knowledge and inadequate management practices among diabetic patients of rural areas of Sindh, especially in patients attending primary healthcare setups. Management techniques and knowhow of this silent and deadly pathological condition should be spread to rural populace through seminars and media, which would eventually mold their life in a better condition.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".