[The Citizen Accompaniment Project for Community Integration for people with traumatic brain injury: a step towards resilience?].
Bibliographic record
Abstract
INTRODUCTION: most people with a traumatic brain injury (TBI) live with physical, sensory, or psychological sequelae that affect their day-to-day functioning and prevent them from performing their regular activities. CONTEXT: a Citizen Accompaniment for Community Integration project (APIC) was implemented for people with TBI to fulfill the lack of access to resources and gives them support to redefine their life projects. OBJECTIVES: this study's aim is to evaluate the APIC's impacts on the participants' wellbeing and their ability to participate in recreational and day-to-day living activities. METHODS: it uses a mixed research design of multiple case studies supported by a participative and collaborative research approach. Qualitative and quantitative datas were collected from 9 participants with TBI in 2 stages, at the beginning of the APIC after 6 months and at the end, after 12 months, using semi-structured interviews. RESULTS: this study shows the APIC's positive impacts in the development of the participant's autonomy and satisfaction with their social participation. DISCUSSION AND CONCLUSION: it tends to reveal that the APIC is a safe space for experimentation, founded on a reciprocal relationship between accompanied and accompanier, and promoting the commitment to the resilience process.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".