Assessment of Diabetes Knowledge Using the Michigan Brief Diabetes Knowledge Test Among Patients With Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Background: Type 2 diabetes mellitus (T2DM) represents a growing health threat globally. The International Diabetes Federation (IDF) estimated that 387 million adults had diabetes in 2014, and this number is expected to continue to grow. In Saudi Arabia, the prevalence of T2DM over time has increased. Diabetes knowledge has been shown to improve self-management skills and glycemic control. The primary goal of this study was to assess diabetes knowledge and its impact on diabetes control and complications. Methods: We conducted a cross-sectional study at King Abdulaziz Specialist Hospital, Taif City, Saudi Arabia, Division of Endocrinology. T2DM patients older than 18 years who underwent a routine visit to the endocrine clinic between June and October 2014 were asked to participate. Baseline characteristics and measurement were obtained at the time of visit. Laboratory data were collected from the patients’ medical records. We excluded patients with type 1 diabetes mellitus (T1DM). We used the Michigan Brief Diabetes Knowledge Test to assess patients’ knowledge. Those patients who answered ? 65% of the questions were considered to possess good knowledge about diabetes. Results: A total of 264 patients participated; 131 (49.6%) were male and 132 (50.0%) were female. Approximately, half of the patients (44.7%) had had diabetes for at least 10 years, and 29.8% of patients had had the disease for 5 - 10 years. The cohort’s mean A1c was 8.56% and mean body mass index was 30.5%. Sixty-four percent of patients had only a high school education or less, and 38% had at least a college degree. Approximately, half of the cohort (41.7%) were considered to be low income, 37.9% were on oral medications only, and 41.3% were on insulin. The mean fraction of correctly answered knowledge questions was 48.26%. Twenty-eight percent of the participants thought that A1c reflected blood glucose control over the past week, and 44.3% did not know what A1c was. Approximately, one-third of patients (29.5%) believed that diet soda could be used to treat low blood glucose. Fifty-seven patients (21.6%) were considered to have good knowledge about diabetes. Conclusion: The majority (78.4%) of the screened T2DM patients had poor knowledge about diabetes. Poor knowledge was associated with higher A1c, a non-significant increase in the majority of measured cardiovascular markers, and less awareness of diabetes-related complications. J Endocrinol Metab. 2017;7(6):185-189 doi: https://doi.org/10.14740/jem473w
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".