Experiences of Informal Caregivers of Older Adults Transitioned From Nursing Homes to the Community Through the Money Follows the Person Demonstration
Bibliographic record
Abstract
This study examined experiences of 156 informal caregivers of older adults who transitioned to the community through the Connecticut Money Follows the Person (MFP) Rebalancing Demonstration after prolonged nursing home stays. Caregiver burden, positive aspects of caregiving, depressive symptoms, anxiety, and comparative subjective stress were examined in relation to caregiver demographics and care receiver characteristics with a cross-sectional survey. Caregivers reported low burden, depressive symptoms, and anxiety, and fairly high levels of positive aspects of caregiving and satisfaction with community services. Most caregivers were less stressed compared to the time the care recipient was in, or before they entered, a nursing home. Live-in caregivers experienced more positive aspects of caregiving, but adult children reported higher burden than other caregivers. Unmet service needs also increased caregiver burden. Programs like MFP are a viable option with broadly positive outcomes from a caregiver's perspective. Enthusiasm for increasing access to community care is growing for older adults who otherwise would reside in nursing homes for extended periods. Identifying unmet service needs and needs for targeted functional support could further enhance caregiver experiences and contribute to the successful transition of older adults to the community.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".