Predictors of diabetes-specific knowledge and attitude among people residing in the urban settlement of Jodhpur
Bibliographic record
Abstract
Background: Diabetes has progressively increased in India and around the world over the last quarter-century, with India accounting for a significant portion of the worldwide burden. Researches show that diabetes mellitus related complications can be reduced by early diagnosis of the disease and appropriate treatment. This study aimed to investigate diabetes-related knowledge, attitudes, and practices in adults in the high-income, middle-income, and low-income groups in families of Jodhpur and to create awareness among the community about diabetes.Methods: With the use of an adequately constructed and validated questionnaire, the current cross-sectional study was conducted on the general population of Rajasthan. The questionnaire was pre-tested and pre-validated. The data were statistically analysed using SPSS.Results: There were 53.3% males and 46.8% females who were enrolled in the study. The mean knowledge score was 8.82±3.467 and the mean attitude score was 3.62±1.439. Respondents who were educated at least till high secondary or above were significantly more knowledgeable and with more attitude scores as compared to people who were either illiterate or educated only up to secondary.Conclusions: We discovered a reasonable gap between knowledge, attitudes, and practices, thus formulating and implementing strategies to transform positive attitudes into helpful practices is the need of the hour.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".