De la perspective d’« aidant naturel » à celle de « proche-soignant » : un passage nécessaire
Bibliographic record
Abstract
The current article shows that the medical and technological progress of the last twenty years has literally changed the contribution of care that is assumed by close relatives of people living with disabilities. In fact, this progress has had as much impact on the rise, the length and the burden of this contribution than the development of the expertise of these caregivers. Whileas in Québec, the designation of natural caregiver is largely used to refer to close relatives and friends who take care, we will see here that their daily life is directly affected by this progress and implies the search for a new balance between the realities of being close to someone and being a caregiver, which the reference to the new appellation "close caregiver" wants to underline.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.043 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".