Feasibility of Recruiting Spouses With DSM-IV Diagnoses for Caregiver Interventions
Bibliographic record
Abstract
BACKGROUND: Reviews and meta-analyses suggest that caregiver interventions have only been modestly effective in reducing caregiver distress. One possible reason is that many intervention studies have recruited heterogeneous caregivers with subclinical symptoms. This study examined the feasibility of recruiting a more homogenous group of caregivers with high clinical distress levels for an intensive therapy intervention. METHODS: During the 2-year study and under ideal circumstances, we recruited caregivers of community-dwelling older adults with dementia for group cognitive behavioral therapy at a University of Toronto affiliated and internationally recognized geriatric health sciences center. We used strict eligibility criteria to recruit primary spouse caregivers with a DSM-IV diagnosis, normal cognitive functioning, and clinically significant distress levels. RESULTS: Of the 97 caregivers screened, 61 were ineligible or uninterested. The 36 interested caregivers who met screening criteria completed a diagnostic intake assessment and only 28 were eligible to begin therapy. DISCUSSION: These results indicate that it would be extremely difficult for clinicians or researchers working in smaller cities or health care centers to run caregiver intervention groups using strict entrance criteria such as those employed in this study. The results of this study provide further support for the importance of diverse and tailored caregiver interventions.
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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 | 0.137 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".