Cadre stratégique pour soutenir l’évaluation des projets complexes et innovants en santé numérique
Bibliographic record
Abstract
Digital technologies play a central role in strategies to improve access, quality and efficiency of health care and services. However, many digital health projects have failed to become sustainable and spread across health organizations and systems. This situation is partly due to the fact that these projects are often developed and evaluated by reducing the issues linked mainly to the technological dimension. Such tradition has paid little attention to the fact that technology is introduced into pluralistic and complex sociotechnical systems such as health organizations and systems. The aim of this article is to propose practical and theorical, non-prescriptive, elements of reflection that can serve as a basis for evaluating complex and innovative digital health projects. This reflection builds on the lessons learned from the application of a strategic framework for evaluating three major complex and innovative digital health projects in Quebec over the last 15 years.
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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 | 0.140 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.022 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".