Burden among caregivers of service members and veterans following traumatic brain injury
Bibliographic record
Abstract
OBJECTIVES: To determine the (a) health status and caregiving appraisal and (b) influence of perceived burden on health and appraisal in a sample of caregivers helping service member/veterans (SMVs) following a traumatic brain injury (TBI). METHODS: Participants were caregivers (N = 283, female = 96.1%, 86.2% = spouse/partner) of SMVs who sustained a mild-severe or penetrating TBI. Participants completed the Caregiver Appraisal Scale (CAS) and the SF-36v2 Health Survey (SF-36v2). Participants were divided into three burden groups: high, neutral, and low. RESULTS: Almost half the sample (48.8%) reported negative feelings on the CAS Perceived Burden scale. A substantial proportion had lower scores than a normative sample on four SF-36v2 physical health scales (35.1-64.5%) and four mental health scales (70.7-79.8%). A significant main effect was found across caregiver burden groups on three CAS scales (p = 0.010 to p < 0.001), two SF-36v2 component scores (all ps < 0.001), and eight SF-36v2 scales (p = 0.001 to p < 0.001). Caregivers with high perceived burden reported significantly worse scores, except CAS Caregiving Ideology. All CAS and SF-36v2 scales were significant predictors of CAS Perceived Burden scores (all ps< 0.001), with the SF-36v2 Social Functioning scale accounting for the most variance (32.6%). CONCLUSIONS: Health care and social services are needed for caregivers who help SMVs to foster resilience, wellness, and growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".