Challenges and facilitators in the experience of caregiving for an older adult with traumatic brain injury: A longitudinal qualitative study in the first-year postinjury.
Bibliographic record
Abstract
PURPOSE: To obtain a better understanding of the factors which complicate or facilitate the adjustment of caregivers after traumatic brain injury (TBI) in older adults. RESEARCH METHOD: At 4, 8, and 12 months post-TBI (mild to severe), 65 caregivers answered two open-ended questions regarding facilitators and challenges linked to the injury of their loved one. A thematic analysis was performed. RESULTS: Participants mentioned almost as many facilitators as challenges at each time point. Among the facilitators, we found the following themes: receiving social support, having access to rehabilitation, improvement of the injured loved one's health condition, returning to live at home, having access to home services, feeling useful, effective communication, and having time for oneself. The challenges identified were: health issues in the injured loved one, psychological impact on the caregiver, assuming a new role, relationship strain, and decrease in activities and outings. CONCLUSIONS: During the first year following TBI in older adults, caregivers were able to identify several facilitators despite the presence of challenging factors, suggesting effective coping and resilience. This knowledge can guide potential caregivers in their adaptation after TBI in an older adult, and we propose a simple tool to support this process. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".