Diabetes education mobile APP prototype for Hispanic communities
Bibliographic record
Abstract
When compared with the general United States population, Hispanic Americans are at an increased risk of developing type 2 diabetes mellitus (DM2) and are far more likely to suffer devastating complications related to the disease. The purpose of this quality improvement project was to determine whether the use of a culturally tailored, mobile application prototype educational tool increased DM2 prevention knowledge among Hispanic patients at risk for DM2. The educational tool contained information about DM2 including risk factors, prevention, and health maintenance. The prototype was developed to function like a working mobile application and a pre/posttest was administered to participants at three local Hispanic community health fairs in Cincinnati, Ohio. Paired t test analysis of the 27 completed surveys showed a statistically significant improvement in posttest scores. The results showed that the average score was 4.1 out of a total of five possible points in the pre-test. The mean total score of the post-test was 4.7, with a total improvement of the mean score of 0.6 (0.0001). It was concluded that there was a statistically significant improvement in the knowledge of DM2 prevention after reviewing the material presented in the application prototype. In addition, participants expressed a strong interest in a working mobile application that offers culturally tailored DM2 prevention education.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".