A Telehealth Approach to Caregiver Self-Management Following Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: To determine whether a telephone-based, individualized education and mentored problem-solving intervention would improve outcomes for caregivers of persons with traumatic brain injury (TBI). DESIGN: Parallel group, randomized controlled trial with blinded outcome assessment. SETTING: General community. PARTICIPANTS: A total of 153 caregivers (mean age = 49.7 years; 82% female; 54% spouses/partners, 35% parents) of persons with moderate to severe TBI who received acute and/or rehabilitation care at a level I trauma center. Eighty-two percent of participants were evaluated at 6-month follow-up. INTERVENTION: Individualized education and mentored problem-solving intervention focused on caregivers' primary concerns delivered via up to 10 telephone calls at 2-week intervals. MAIN OUTCOME MEASURES: Composite of Bakas Caregiving Outcomes Scale (BCOS) and Brief Symptom Inventory (BSI-18) at 6 months post-TBI survivor discharge. Secondary measures included the Brief COPE. RESULTS: Caregivers in the treatment arm scored higher on the BCOS-BSI composite (P = .032), with more active coping (P = .020) and less emotional venting (P = .028) as measured by the Brief COPE. CONCLUSIONS: An individualized education and mentored problem-solving approach delivered via telephone in the first few months following community discharge of the TBI survivor resulted in better caregiver outcomes than usual care. Consideration should be given to using this approach to augment the limited support typically offered to caregivers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".