Predictors of Preference for Hospice Care Among Diverse Older Adults
Bibliographic record
Abstract
UNLABELLED: The purpose of this study was to identify predictors of preference for hospice care and explore whether the effect of these predictors on preference for hospice care were moderated by race. METHODS: An analysis of the North Carolina AARP End of Life Survey (N = 3035) was conducted using multinomial logistic modeling to identify predictors of preference for hospice care. Response options included yes, no, or don't know. RESULTS: Fewer black respondents reported a preference for hospice (63.8% vs 79.2% for white respondents, P < .001). While the proportion of black and white respondents expressing a clear preference against hospice was nearly equal (4.5% and 4.0%, respectively), black individuals were nearly twice as likely to report a preference of "don't know" (31.5% vs 16.8%). Gender, race, age, income, knowledge of Medicare coverage of hospice, presence of an advance directive, end-of-life care concerns, and religiosity/spirituality predicted hospice care preference. Religiosity/spirituality however, was moderated by race. Race interacted with religiosity/spirituality in predicting hospice care preference such that religiosity/spirituality promoted hospice care preference among White respondents, but not black respondents. CONCLUSIONS: Uncertainties about hospice among African Americans may contribute to disparities in utilization. Efforts to improve access to hospice should consider pre-existing preferences for end-of-life care and account for the complex demographic, social, and cultural factors that help shape these preferences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".