Comparing Palliative Care Knowledge in Metropolitan and Nonmetropolitan Areas of the United States: Results from a National Survey
Bibliographic record
Abstract
Background: Despite recent growth in access to specialty palliative care (PC) services, awareness of PC by patients and caregivers is limited and misconceptions about PC persist. Identifying gaps in PC knowledge may help inform initiatives that seek to reduce inequities in access to PC in rural areas. Objective: We compared knowledge of PC in metropolitan and nonmetropolitan areas of the United States using a nationally representative sample of U.S. adults. Design: We used data from the 2018 Health Information National Trends Survey (HINTS) 5 Cycle 2 to compare prevalence and predictors of PC knowledge and misconceptions in nonmetropolitan and metropolitan areas as defined by the 2013 Urban–Rural Classification (URC) Scheme for Counties. We estimated the association between nonmetro status and knowledge of PC, adjusted for respondent characteristics, using multivariable logistic regression. Results: More respondents reported that they had never heard of PC in nonmetro (78.8%) than metro (70.1%) areas ( p < 0.05). Controlling for other factors, nonmetro residence was associated with a 41% lower odds of PC knowledge (odds ratio [OR] = 0.59; 95% confidence interval [CI] = 0.37–0.94), and Hispanic respondents also demonstrated significantly lower odds of PC knowledge conditional on rural status (OR = 0.47; CI = 0.27–0.83). Misconceptions about PC were high in both metro and nonmetro areas. Conclusion: Awareness of PC was lower in rural and micropolitan areas compared with metropolitan areas, suggesting the need for tailored educational strategies. The reduced awareness of PC among Hispanic respondents regardless of rural status raises concerns about equitable access to PC services for this population.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".