Accompagnement citoyen personnalisé en intégration communautaire : un défi pour la santé mentale ?
Bibliographic record
Abstract
APIC (Citizen Accompaniment Project for Community Integration) offers support for the social integration of people living with traumatic brain injury. The accompanying citizen meets the person three hours a week for a period of a year in order to offer assistance in the accomplishment of his/her projects and activities. This role confronts the accompanying citizen with many challenges that may put their mental health at risk. This article offers a reflection on this practice from the accompanying citizen's perspective. Five principles that can help better delimit and define citizen accompaniment are drawn from the results: 1) finding a "good distance" in the relationship to the accompanied person, 2) considering all of the actors in the process, 3) putting the accompanied person and their desires at the heart of the practice, 4) accepting not knowing everything, 5) being committed to the project and accepting it may transform you.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 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; both teacher heads agree on what is shown here.
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".