Geographic disparities associated with travel to medical care and attendance in programs to prevent/manage chronic illness among middle-aged and older adults in Texas
Bibliographic record
Abstract
INTRODUCTION: Accessing care is challenging for adults with chronic conditions. The challenge may be intensified for individuals needing to travel long distances to receive medical care. Transportation difficulties are associated with poor medication adherence and delayed or missed care. This study investigated the relationship between those traveling greater distances for medical care and their utilization of programs to prevent and/or manage their health problems. It was hypothesized that those traveling longer distances for medical care attended greater chronic disease management programs. METHODS: Thirty six thousand households in nine counties of central Texas received an invitation letter to participate in a mailed health assessment survey in English or Spanish. A total of 5230 participants agreed to participate and returned the fully completed survey. To investigate distance traveled for medical services and participation in a chronic disease management program, the analyses were limited to 2108 adults aged ≥51 years with one or more chronic conditions who visited a healthcare professional at least once in the previous year. Other variables of interest included residential rurality, health status, and personal characteristics. The data were first analyzed using descriptive and bivariate analyses. Then, an ordinal logistic regression model was fitted to identify factors associated with longer distances traveled to medical services. Additionally, a binary logistic regression model was fitted to identify factors associated with attending a chronic disease self-management program. RESULTS: Among 2108 adults, rural participants (p<0.001), those with more chronic conditions (p<0.001), and those attending a chronic disease program (p=0.037) reported traveling further distances to medical services. Participants with limited activity (p<0.001), those from urban counties (p=0.017), and those who traveled further (p=0.030) were more likely to attend a chronic disease program. CONCLUSION: While further distances to healthcare providers was found to be a protective factor based on the utilization of community-based resources, rural residents were less likely to attend a program to better manage their chronic conditions, potentially choosing to use long distance travel to address urgent medical needs rather than focusing on prevention and management of their conditions. Important policy and programmatic efforts are needed to increase reach of chronic disease self-management programs and other community services and resources in rural areas and to reduce rural inequities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".