Decision Regret among Informal Caregivers Making Housing Decisions for Older Adults with Cognitive Impairment: A Cross-sectional Analysis
Bibliographic record
Abstract
Background. Informal caregivers are regularly faced with difficult housing decisions for older adults with cognitive impairment. They often regret the decision they made. We aimed to identify factors associated with decision regret among informal caregivers engaging in housing decisions for cognitively impaired older adults. Methods. We performed a secondary analysis of cross-sectional data collected from a cluster-randomized trial. Eligible participants were informal caregivers involved in making housing decisions for cognitively impaired older adults. Decision regret was assessed after caregivers’ enrollment in the study using the Decision Regret Scale (DRS), scored from 0 to 100. We used a conceptual framework of potential predictors of regret to identify independent variables. We performed multilevel analyses using a mixed linear model by estimating fixed effects (β) and 95% confidence intervals (CIs). Results. The mean (SD) DRS score of 296 informal caregivers (mean [SD] age, 62 [12] years) was 12.4 (18.4). Factors associated with less decision regret were having a college degree compared to primary education (β [95% CI]: –11.14 [–18.36, –3.92]), being married compared to being single (–5.60 [–10.05, –1.15]), informal caregivers’ perception that a joint process occurred (–0.14 [–0.25, –0.02]), and older adults’ not having a specific housing preference compared to preferring to stay at home (–4.13 [–7.40, –0.86]). Factors associated with more decision regret were being retired compared to being a homemaker (7.74 [1.32, 14.16]), higher burden of care (0.14 [0.05, 0.22]), and higher decisional conflict (0.51 [0.34, 0.67]). Limitations. Our analysis may not illustrate all predictors of decision regret among informal caregivers. Conclusions. Our findings will allow risk-mitigation strategies for informal caregivers at risk of experiencing regret.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".