« Ressentir, surveiller et comprendre un corps qui change pour s'adapter »
Bibliographic record
Abstract
L'objectif de cet article est de mieux comprendre comment les personnes vivant avec la maladie de Parkinson font usage d'une technologie d'autosoin nommée eCARE-PD<sup>TM </sup>(Electronic Care for Parkinson Disease). À partir des résultats d'une étude qualitative reposant sur la méthode du journal d'usage et des entretiens semi-dirigés, nous identifions la logique d'autosoin qui émerge de cet usage et la manière dont cette logique interroge le processus de conception. Pour ce faire, nous décrivons les pratiques d'autosoin qui émergent de l’usage de cette technologie, en portant une attention particulière à ce que signifie « prendre soin » d'un corps multiple et imprévisible (Mol, 2002). Nous verrons que eCARE-PD<sup>TM</sup> ne se contente pas d'équiper l'autosoin, mais reconfigure les modalités mêmes de l'autosoin dans le cadre de cette maladie chronique, en privilégiant l'adaptation continue au contrôle, et l'attention partagée à l'autonomie individuelle. <strong>Abstract:</strong> This article aims to improve our understanding of how people with Parkinson's disease use eCARE-PD™ (Electronic Care for Parkinson Disease), a self-care technology. Based on the results of a qualitative study using a usage diary method and semi-structured interviews, we identify the self-care logic that emerges from using this technology and how it challenges the design process. We describe the self-care practices that emerge from using this technology, paying particular attention to what it means to "take care" of an unpredictable and multiple body (Mol, 2002). We will see that eCARE-PD™ not only facilitates self-care but also reconfigures its modalities in the context of this chronic disease, prioritizing continuous adaptation over control and distributed attention over individual autonomy.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".