Perspectives of survivors of traumatic brain injury and their caregivers on long-term social integration
Bibliographic record
Abstract
BACKGROUND: Traumatic brain injury (TBI) has damaging impacts on victims and family members' lives and their long-term social integration constitutes a major challenge. PURPOSE: The objective of the study was to document the repercussions of TBI on victims' long-term social integration (10 years post-trauma) and the contribution made by the services received from the point of view of TBI victims and family caregivers. This article examines the determinants of long-term social integration as well as the impact of TBI on family caregivers. METHODS: A qualitative design was used (semi-directed interviews). The sample consisted of 22 individuals who had sustained a moderate or severe TBI and 21 family caregivers. RESULTS: The results show that TBI is an experience that continues to present difficulties, even 10 years after the accident, and that different barriers contribute to this difficulty: not going back to work, depressive episodes, problems in relationships and sequellae. Family caregivers must help TBI victims confront the barriers in their path. IMPLICATIONS: This study adopts a longitudinal perspective to help professionals determine how to intervene with TBI victims and their families. It validates the importance of having clients and family caregivers describe their reality.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".