309 Evaluation of the usability and sustainability of a technological platform for the development of decision aids
Bibliographic record
Abstract
Introduction Decision aid (DA) development requires expertise and resources to engage key stakeholders and meet the International Patient Decision Aids Standards (IPDAS). The PADA technology platform facilitates this process. Our aim was to assess users’ perceptions of the usability and sustainability of a minimal viable version of PADA. Methods In this descriptive mixed-methods study, we recruited researchers through our international networks who were willing to develop their DA in either French, English or Spanish. Participants took part in video-recorded think-aloud sessions while using PADA. After the development process, participants answered semi-structured questions and the Normalisation MeAsure Development (NoMAD) questionnaire to assess the factors influencing the implementation of PADA. Two researchers conducted thematic qualitative analyses of the interview guided by the Normalization Process Theory. Descriptive statistical analyses were performed on questionnaire results. Preliminary Results To date, we have recruited eight participants from three countries, with varying levels of experience, ranging from zero to ten DAs developed in the past. Experienced users quickly understood the features of PADA and were able to use it immediately, while novices needed guidance and additional resources before using it. For some key features, such as previsualization which allows a preview of the DA at any development stage, we had to guide participants to use them. Participants found PADA particularly valuable for getting feedback from collaborators or patients at the prototyping stage and saw it as an incentive to use it in the future. Discussion Less experienced users needed coaching to clarify decision points or templates for content creation before using PADA. A tutorial should be added to familiarize users with all PADA features. Conclusions PADA is suitable for users with diverse experience levels. Adapting PADA based on our findings will ensure that it meets the needs of a broad user base in the future.
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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 | 0.021 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".