DEVELOPMENT OF CONSUMER-REPORTED QUALITY INDICATORS FOR PUBLICLY-FUNDED HOME AND COMMUNITY-BASED SERVICES
Bibliographic record
Abstract
Abstract Consumer-reported measures are important quality indicators of person-centered home and community-based services (HCBS). Lack of granularity in administrative data hinders measurement of consumer-reported quality in publicly-funded HCBS. The National Core Indicators- Aging and Disability Survey (NCI-AD) is potentially a great source of consumer-reported data for quality measurement in publicly-funded HCBS. Therefore, we analyzed NCI-AD data to develop consumer-reported quality indicators for HCBS. We applied exploratory factor analyses (EFA) to consumer-reported items from the 2018-2019 survey wave (n=5,572 community-dwelling consumers, age ≥65 years). Through EFA, we identified 5 quality indicators which were then validated in two ways, qualitatively by a technical expert panel (for face validity and contextualization) and quantitatively, using confirmatory factor analyses in the 2017-2018 survey wave (n=9,145 community-dwelling consumers, age ≥65 years). The 5 newly developed and validated quality indicators included service satisfaction (3 survey items, Cronbach’s alpha=0.89, McDonald’s omega=0.90), staff quality (5 survey items, alpha=0.86, omega=0.89), environmental safety (4 survey items, alpha=0.81, omega=0.89), service decision-making (5 survey items, alpha=0.79, omega=0.86), and community inclusion (5 survey items, alpha=0.78, omega=0.86). These indicators had reasonable concurrent validity, with service satisfaction having the highest correlation with other indicators (Pearson’s correlation coefficient between 0.59-0.36). These five new consumer-reported quality indicators can be used by researchers, service providers, and policy makers for evaluating and monitoring service quality and identifying modifiable targets for quality improvement in publicly-funded HCBS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".