Characterizing post-traumatic growth in individuals with traumatic brain injury according to social participation, self-awareness, and self-identity
Bibliographic record
Abstract
Purpose After traumatic brain injury (TBI), individuals may face challenges in their social participation, self-awareness, and self-identity. However, positive life changes can also be experienced (i.e., post-traumatic growth). This study aimed to characterize the social participation, self-awareness, and self-identity of individuals with TBI displaying post-traumatic growth.Materials and methods Fifteen participants (male = 10, mean age = 49.7 years) with moderate to severe TBI (average years post-injury = 15.2) were included in this mixed-methods study. Self-report questionnaires were used to assess social participation, self-awareness, and self-identity. Qualitative data, collected using semi-structured interviews, were used to categorize participants into two groups: higher (n = 8) and lower (n = 7) post-traumatic growth. Descriptive statistics were used to characterize participants in each group in terms of their social participation, self-awareness, and self-identity.Results Participants with higher post-traumatic growth had increased social participation, higher self-awareness, and fewer negative discrepancies between their pre- and post-injury self-identities, compared to participants with lower post-traumatic growth.Conclusion This study contributes to a more comprehensive understanding of post-traumatic growth through the use of both qualitative and quantitative data. These findings can inform future research and development of programs to promote post-traumatic growth post-TBI.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".