Clusters of home- and community- based service use and association with quality of life in older adults
Bibliographic record
Abstract
Abstract Measuring the quality of home- and community-based services (HCBS) is challenging because people use a diverse array of services. To fill this gap we evaluated whether distinct clusters of older adults exist based on their patterns of HCBS use, and whether consumer-reported quality of life (QoL) varies between these clusters, with or without dementia. We included 775 Medicaid HCBS consumers in Minnesota who also participated in the 2017-2018 National Core Indicators-Aging & Disability Adult ConsumerSurvey (NCI-AD). We linked NCI-AD respondents with their Medicaid claims. A validated QoL score was constructed using factor analysis from items in the NCI-AD. The QoL score includes items reflecting care experiences, security, and autonomy. Principal component analysis identified clusters of consumers with similar patterns of HCBS use (home healthcare, non-medical transportation, personal care assistance, adult day care, durable medical equipment, and homemaker services). We evaluated differences in mean QoL scores between clusters and within each cluster among consumers with and without dementia, using linear regression. Four clusters were identified based on patterns in service type. The clusters varied in composition based on race/ethnicity, proportion with dementia, and functional dependence. In regression analyses, mean QoL scores were similar between clusters. Within each cluster, mean QoL scores were also similar between those with and without dementia. These findings indicate that despite significant heterogeneity in patterns of HCBS use, QoL scores can be used reliably as a standard quality indicator for all HCBS consumers, regardless of dementia diagnosis, to inform HCBS quality assurance efforts.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".