The development of a web‐based module on Gestational Diabetes Mellitus for patients and healthcare professionals
Bibliographic record
Abstract
Gestational Diabetes Mellitus (GDM), the most common endocrine disorder during pregnancy, poses short‐term and long‐ term risks for mother and baby. However, intense management of GDM has been shown to reduce negative maternal and fetal outcomes. Such management requires that both healthcare providers and patients are capable of making informed decisions in the control of GDM. Reports from local diabetes clinics and medical professionals suggest healthcare providers’ knowledge of GDM is unsatisfactory. The objective of this project was to create an interactive web‐based module to educate medical students and to provide an effective resource for healthcare providers. The case‐based, self‐study tool was designed using Adobe Dreamweaver Creative Suite 3 and Articulate Studio 09. The modular content draws from the current Clinical Practice Guidelines established by the Canadian Diabetes Association, and will be available to medical students, practicing physicians, and women with GDM. Formative feedback from a questionnaire of the module is anticipated for the spring. We expect the GDM module will educate healthcare providers and patients on the prevalence and risk factors, as well as, the importance of early screening, treatment and prevention of GDM. This empowerment tool should encourage educated decision making, facilitate the management of GDM, and curtail known risks associated with poorly managed GDM. Grant Funding Source : Internal
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.009 |
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".