Telephone-delivered psychosocial intervention reduces burden in dementia caregivers
Bibliographic record
Abstract
OBJECTIVES: To examine the preliminary efficacy of Family Intervention: Telephone Tracking-Dementia (FITT-D), a multi-component intervention that is delivered in 23 telephone contacts over 12 months. METHOD: Thirty-three dementia caregivers were randomly assigned to receive either FITT-D (n =16) or standard care (n =17) using urn randomization to balance the groups on dementia severity, caregiver gender, and relationship type (spouse versus other). Inclusion criteria included formal dementia diagnosis, caregiving for at least 6 months, residing with the care recipient, and providing at least 4 hours of direct supervision per day. Master's-level therapists contacted caregivers by telephone over 12 months. Each contact followed a standardized treatment manual, involving assessment and individualized application of interventions to address mood, family functioning, social support, and health. Outcomes included Zarit Burden Interview, Revised Memory and Behavior Problem Checklist, and the Geriatric Depression Scale at baseline and 12 months (end of treatment). RESULTS: Caregivers receiving FITT-D exhibited significantly lower burden scores and less severe reactions to memory and behavior problems than caregivers in the standard care condition. CONCLUSION: Findings provide preliminary evidence for the efficacy of FITT-D, a potentially highly accessible, low-cost intervention for dementia 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".