T he The Diabetes Communicator An Education, Research and Support Forum for Canadian Diabetes Educators JULY/AUG 2006
Bibliographic record
Abstract
advent of new technology brings excitement but also new challenges for people with diabetes and diabetes educators. These challenges represent the human experience of technology and range from issues such as learning and mastering new concepts and skills to developing selfefficacy and comfort in living with technological developments. Understanding technology in diabetes includes considering its particular relevance and appropriateness in relation to individual interests, needs and circumstances.There are also various ethical and advocacy issues to consider, including cost, affordability and access to technology. Diabetes management today is impressive for the various technological developments that are providing new methods and insights for blood glucose (BG) control as well as new ways to learn about diabetes.This issue of The Diabetes Communicator highlights just a few of the innovative and amazing forms of technology that have been developed to improve diabetes management.As we consider these new technologies, we will be faced with new opportunities as well as new questions and challenges. All of this is part of what makes diabetes education and care such an exciting field of study and work. With the arrival of innovatove tools in diabetes technology, including real-time glucose sensors, we are embarking on an unprecedented period in diabetes education and management. Never before have people with diabetes been able to see, minute by minute, 24 hours a day, what is going on inside their bodies with (BG) changes. Previously invisible BG levels are now visible at a glance and the human response to this has yet to be fully understood.This new technology, like most forms of technology, brings new questions while it seeks to
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.138 | 0.029 |
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".