“You’re Left on Your Own”: A Qualitative Study on the Experiences of Community Integration After Traumatic Brain Injury
Bibliographic record
Abstract
Background: Achieving meaningful community integration (engagement in meaningful activity, independent living, and social connectedness) after a traumatic brain injury (TBI) requires addressing persistent barriers limiting its fulfillment. This qualitative study explored the perceptions and experiences of community integration for individuals living with TBI in the community. Methods: Using semi-structured interviews, four focus groups of individuals with TBI were conducted. Data were analyzed using codebook thematic analysis. Findings: There were 13 participants between the ages of 25 and 64, who had acquired their injury at least three years earlier. Community integration was illustrated through three themes: (1) ‘Am I left on my own?’ explored the support systems after TBI, (2) ‘One size fits all’ described the response of society to TBI, and (3) ‘Adapting to a new normal’ highlighted responses to a changed reality. Conclusions: Individuals with TBI reported decreased community integration in multiple facets of life. Understanding the experiences of community integration after TBI can create room for future rehabilitation interventions that consider new abilities and adaptation to barriers.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".