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Record W4400951585 · doi:10.1177/08982643241267378

Measuring Consumer-Reported Quality of Life Among Recipients of Publicly Funded Home- and Community-Based Services: Implications for Health Equity

2024· article· en· W4400951585 on OpenAlexaff
Tetyana Shippee, Yinfei Duan, Zachary G. Baker, Romil R Parikh, Taylor Bucy, Eric Jutkowitz

Bibliographic record

VenueJournal of Aging and Health · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Alberta
FundersNational Institute on Aging
KeywordsSpouseGerontologyQuality of life (healthcare)Ethnic groupEquity (law)Latent class modelMedicineHealth equityPsychologyPublic healthNursing

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.376
GPT teacher head0.503
Teacher spread0.127 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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