Exploring the Use of a Participative Design in the Early Development of a Predictive Test: The Importance of Physician Involvement
Bibliographic record
Abstract
In this study, we contribute to the personalized medicine and health care management literature by developing and testing a new participative design approach. We propose that involving gastroenterologists in the development of a predictive test to assist them in their clinical decision-making process for the treatment of inflammatory bowel diseases will increase the likelihood of their acceptance of the innovation. Based on data obtained from 6 focus groups across Canada from a total of 28 physicians, analyses reveal that current tools do not enable discriminating between treatment options to find the best fit for each patient. Physicians expect a new predictive tool to have the capability of showing clear reliability and significant benefits for the patient, while being accessible in a timely manner that facilitates clinical decisions. Physicians also insist on their key role in the implementation process, hence confirming the relevance and importance of participative designs in personalized medicine.
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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 | 0.415 | 0.458 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".