Information behaviour of people with type 1 diabetes in Germany
Bibliographic record
Abstract
In recent years there have been numerous technical innovations such as CGM systems or insulin pumps that have made life easier for people with type 1 diabetes. However, this also means that more and more information is available. The aim of the present study is to find out more about the daily handling of information. The following research question was asked: What information do people with type 1 diabetes use? To answer this research question, a quantitative online survey of people with type 1 diabetes was conducted by Prof. Dr. Matthias Fank at the Technical University of Cologne. The online survey mainly consisted of 25 closed questions, which were asked on a scale from 0 to 10. The responses of 1,025 people who are at least 18 years old were included in the evaluation. The most important information for type 1 diabetics is the "current value". 67.5% have this on Place 1 placed. Current glucose levels are provided by CGM systems used by 94.2% of people with type 1 diabetes. Quarterly visits to the diabetologist are important and provide important information. 30.8% “completely” agree with this statement on a scale from 0 to 10. Only 2.2% of people with type 1 diabetes are satisfied with their current diabetes management apps. There is a desire for a manufacturer-independent app. The strongest agreement with a value of 10 was chosen by almost a quarter (24.6%) of the people with type 1 diabetes. The study provides an insight into diabetes therapy and shows the need for action.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".