Accompanying citizen of persons with traumatic brain injury in a community integration project: An exploration of the role
Bibliographic record
Abstract
BACKGROUND: The APIC (Citizen Accompaniment Project for Community Integration) is an innovative program that seeks to increase the community integration of people with a traumatic brain injury (TBI) by offering the support of an accompanying citizen (AC) in the accomplishment of significant life activities. OBJECTIVE: This article aims to better define the AC's role in this process, by underlining the mental health risks they face, the strategies they use, and the impacts the role has on their own lives. METHODS: This qualitative study is based on the analysis of interviews conducted with the AC following the first year of implementation. The data is part of a larger project aimed at the implementation and evaluation of APIC program. The data were analysed according to the rules of thematic content analysis, which encourages grouping main ideas into categories. This is an iterative process allowing for the constant emergence of new categories during the analysis. RESULTS: Accompaniment is full of joys and positive moments for the AC and participants. However, some difficult situations are encountered and the AC are constantly faced with challenges and constraints. The emerging study themes are as follows: the environment, the characteristics of the person accompanied, the AC's personal limits, and the ambiguities in the accompanier-accompanied relationship. CONCLUSIONS: Five principles drawn from the results will help oversee this practice more efficiently and limit the mental health risks of accompaniers. These findings may permit further development of this type of program for people living with disabilities.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".