A computational validation for the health concept maturity levels questionnaire
Arthur Trognon, Islem Habibi, Hamza Altakroury, Loann Mahdar-Recorbet, Alizée Poli, David Servais, Julien Lambert, Mathias Béjean, Denis Abraham · 2026
Read A computational validation for the health concept maturity levels questionnaire by Arthur Trognon, Islem Habibi, Hamza Altakroury, Loann Mahdar-Recorbet, Alizée Poli, David Servais, Julien Lambert, Mathias Béjean, Denis Abraham (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12884647.txt.
Description
Arthur Trognon and co-authors test a questionnaire that rates how mature a health product concept is, using machine learning as a new kind of validity check. Living Labs, where users help shape health innovations, need tools to judge concepts early. Their questionnaire has a very large item set built around maturity factors.
The team annotated expert speech about maturity levels, trained models to spot the right factors in text, and fed those results into a neural network. Models trained on the true factors found the matching factor in questionnaire items, while control models did not. One domain showed weak or artificial performance. The authors call this computational semantic validity and note two factors still overlap too much.
Catalog record
| CREATOR | Arthur Trognon, Islem Habibi, Hamza Altakroury, Loann Mahdar-Recorbet, Alizée Poli, David Servais, Julien Lambert, Mathias Béjean, Denis Abraham |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 57 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |