Predictors of Access to Healthcare: What Matters to Rural Appalachians?
Bibliographic record
Abstract
OBJECTIVE: Lack of access to healthcare is frequently cited as a primary reason for health disparities globally, especially in poor, rural areas such as Appalachia in the U.S. This study examined predictors of perceived access to healthcare among residents in a poor, medically underserved, rural Appalachian community. METHODS: The study was guided by the revised behavioral model of healthcare services utilization. Self-reported survey data were obtained from a convenience sample of 921 residents in rural Tennessee. RESULTS: The majority of respondents in this study did not perceive access to healthcare to be a problem in their community. Financial factors, health status, and associated social factors negatively affected only a small number respondents' perceptions of access to healthcare. CONCLUSIONS: Despite the presence of multiple factors previously shown to affect access to healthcare, the majority of respondents in this study did not perceive access to healthcare to be a problem in their community. Results of this study suggest that to understand an individual's passage through the healthcare system, the contextual aspects of healthcare utilization, should be added to coverage, services, timeliness, and workforce as a fifth component of access to healthcare. Assessing perceived need and associated cultural factors that affect individuals' concepts of health and wellness represent important areas for future exploration to explain observed health disparities. Additionally, findings showed that having sufficient quality and quantity of healthcare professionals and services in a community or region may be necessary, but not sufficient to explain health disparities and the underlying reasons why individuals choose or choose not to seek health services.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".