Traumatic brain injury caregivers: A qualitative analysis of spouse and parent perspectives on quality of life
Bibliographic record
Abstract
The objective of this qualitative study was to examine how family caregivers of individuals with traumatic brain injury (TBI) describe their quality of life in the context of their caregiving role. Fifty-two caregivers of adults with moderate or severe TBI (n = 31 parents, n = 21 partners/spouses; 77% female; mean age = 57.96 years, range = 34-78 years) were recruited from three data collection sites to participate in focus groups. Thematic content analysis was used to identify two main meta-themes: Caregiver Role Demands and Changes in Person with TBI. Prominent sub-themes indicated that caregivers are (1) overburdened with responsibilities, (2) lack personal time and time for self-care, (3) feel that their life is interrupted or lost, (4) grieve the loss of the person with TBI, and (5) endorse anger, guilt, anxiety, and sadness. Caregivers identified a number of service needs. A number of sub-themes were perceived differently by partner versus parent caregivers. The day-to-day responsibilities of being a caregiver as well as the changes in the person with the TBI present a variety of challenges and sources of distress for caregivers. Although services that address instrumental as well as emotional needs of caregivers may benefit caregivers in general, the service needs of parent and partner caregivers may differ.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".