Nursing Home Resident Preferences for Daily Care and Activities: A Latent Class Analysis of National Data
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Uncovering subgroups of nursing home residents sharing similar preference patterns is useful for developing systematic approaches to person-centered care. This study aimed to (i) identify preference patterns among long-stay residents, and (ii) examine the associations of preference patterns with resident and facility characteristics. RESEARCH DESIGN AND METHODS: This study was a national cross-sectional analysis of Minimum Data Set assessments in 2016. Using resident-rated importance for 16 preference items in the Preference Assessment Tool as indicators, we conducted latent class analysis to identify preference patterns and examined their associations with resident and facility characteristics. RESULTS: We identified 4 preference patterns. The high salience group (43.5% of the sample) was the most likely to rate all preferences as important, whereas the low salience group (8.7%) was the least likely. The socially engaged (27.2%) and the socially independent groups (20.6%) featured high importance ratings on social/recreational activities and maintaining privacy/autonomy, respectively. The high salience group reported more favorable physical and sensory function than the other 3 groups and lived in facilities with higher staffing of activity staff. The low salience and socially independent groups reported a higher prevalence of depressive symptoms, whereas the low salience or socially engaged groups reported a higher prevalence of cognitive impairment. Preference patterns also varied by race/ethnicity and gender. DISCUSSION AND IMPLICATIONS: Our study advanced the understanding of within-individual variations in preferences, and the role of individual and environmental factors in shaping preferences. The findings provided implications for providing person-centered care in NHs.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".