Penser les usages projetés, concrets et situés des technologies numériques de santé
Bibliographic record
Abstract
Dans un contexte de déploiement massif des technologies de santé, ce numéro de la revue COSSI aborde les enjeux liés à l'usage de ces technologies et souligne la nécessité d'analyser le décalage entre les usages projetés et les modalités d'appropriation réelle en contexte de soins. Bien que ces dispositifs visent à optimiser la performance des systèmes de soins face aux maladies chroniques et vieillissement de la population, leur intégration dans des pratiques de soins et cliniques demeure imprévisible et dépend de contextes sociotechniques complexes. Le numéro s'articule autour de trois axes : (a) la renégociation du travail du patient et des soignants lors de l'usage de technologies d'autosurveillance, (b) l'étude des usages projetés et réels des systèmes dits « intelligents » (c) la reconfiguration de la relation thérapeutique et l'émergence de nouvelles formes de proximité à distance. Abstract: In the context of the expanding implementation of health technologies, this particular issue of the Revue COSSI explores the challenges associated with the use of these technologies and underscores the critical need to examine the discrepancy between intended uses and the actual modalities of appropriation within healthcare contexts. These devices aim to optimize healthcare system performance in response to chronic diseases; however, their integration into clinical and care practices remains unpredictable and depends on complex sociotechnical contexts. This issue is focused on three main areas: (a) The renegotiation of patient and caregiver work through the use of self-monitoring technologies; (b) The analysis of the intended and real uses of "intelligent" systems; (c) Finally, the reconfiguration of the therapeutic relationship and the creation of new ways for people to connect remotly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".