Innovating in partnership by creating a mobile application decision aid in the doctor-patient relationship: The ApiAppS research
Bibliographic record
Abstract
This article presents a research funded by the French National Research Agency (ANR) on the design of a prescription decision aid for mobile health applications for French general practitioners. This research, proposed by an inter-university consortium, has become an interdisciplinary development in partnership with patients. The article sheds light on both the phases constituted mobilizing in turn, the different researchers, professionals, citizens and patients, the modalities of partnership initiated as well as the results of the research. Cet article présente une recherche financée par l’Agence Nationale de la Recherche (ANR) sur la conception d’une aide à la décision de prescription d’applications mobiles de santé pour les médecins généralistes français. Proposée par un consortium interuniversitaire, cette recherche est devenue, en cours d’élaboration, interdisciplinaire, en partenariat avec les patients. L’article éclaire tant les phases constituées mobilisant à tour de rôle les différents chercheurs, professionnels, citoyens et patients, les modalités de partenariat initiés, que les résultats de la recherche.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".