Retrospective Pilot Feedback Survey of 200 Users of the AIDA Version 4 Educational Diabetes Program. 3—Discussion.
Bibliographic record
Abstract
This column continues the report of a detailed, questionnaire-based, post-release feedback survey of 200 users of the AIDA version 4 educational diabetes simulator. AIDA is a software program—available without charge from www.2aida.org—that permits the interactive simulation of plasma insulin and blood glucose profiles for educational, demonstration, self-learning, and research purposes. The main goals of the questionnaire-based survey were: (i) to establish what people have thought about the AIDA software, (ii) to assess the utility of the program, and (iii) to ascertain how much people have actually used it. Three ancillary study objectives were: (a) to identify any problems that users may have experienced with the software since its launch, with the intention being for any critical problems to be addressed in future upgrades; (b) to identify features that users felt were important to have incorporated in later releases of the program; and (c) to establish the feasibility of undertaking such a post-release diabetes software survey primarily via electronic mail. In Part 1 of this column ( Diabetes Technol Ther 2006;8:419–432) an analysis was undertaken of quantitative data from the first 200 feedback forms that were returned by AIDA users. In Part 2 of the column ( Diabetes Technol Ther 2006;8:602–608) qualitative feedback comments left by users as part of the questionnaire-based survey were overviewed. The present column discusses the findings of the survey and highlights issues for future consideration. One concept that is introduced is the drug paradigm for assessment of medical software. Even after a drug has been tested, evaluated, and released—pharmaceutical firms still have systems in place to monitor usage and any complications. It is proposed that similar concepts should apply for medical informatics tools and software—with post-release monitoring of programs taking place routinely. This approach is exemplified for the AIDA software by data from the current post-release monitoring survey.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".