Measuring Consumer-Reported Quality of Life Among Recipients of Publicly Funded Home- and Community-Based Services: Implications for Health Equity
Bibliographic record
Abstract
Objectives Despite an increased policy focused on home- and community-based services (HCBS), little is known about their quality of life (QoL)—a key measure of person-centered care. This paper addresses this gap by measuring consumers’ self-reported QoL and identifying factors associated with disparities in QoL. Methods We analyzed the 2015–2016 National Core Indicators–Aging and Disability survey for 3426 respondents in Minnesota, using factor analyses to identify latent QoL domains. Multivariable regression models identified predictors of QoL domains. Results Factor analyses identified three valid and reliable latent QoL domains: security, self-determination, and care experiences. Younger consumers with disabilities (versus consumers ≥65 years of age), minoritized racial/ethnic groups, consumers with hearing loss, without a spouse/domestic partner, and not living in consumer’s own/family home reported significantly lower QoL in various domains ( p < .001). Discussion Disparities in HCBS consumer-reported QoL exist, necessitating equitable reforms to improve HCBS quality for its increasingly diversified consumer base.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".