Relationship Between Type of Health Insurance and Access to Care Among African American Males
Bibliographic record
Abstract
Insurance based discrimination is a bias that has the potential to harm patients by limiting their access to care and widen health disparities between patients with private and public insurance. The purpose of this study was to examine whether there is a relationship between type of health insurance (private and public) and access to care factors (acceptability and availability) among African American males ages 25–64 with chronic conditions. The Andersen healthcare utilization model framework supported this research in focusing on the determinants that influences a patient’s ability to access to health care. The research questions examined whether a relationship existed between type of health insurance (public and private) and acceptability (told by a doctor that they do not accept your coverage) and availability (trouble getting an appointment at a doctor’s office or clinic, etc.) among African American males ages 25–64 with chronic conditions. The research questions were answered with secondary data collected from the Health Reform Monitoring Survey 3rd Quarter 2018. This quantitative retrospective study involved a descriptive comparative research design using chi-square. Findings showed that there was no statistically significant relationship between type of health insurance and acceptability (p > .05), nor was there a statistically significant relationship between type of health insurance and availability (p > .05), among African American males ages 25–64 with chronic conditions. The implication for positive social change of this research includes information related to patient prioritization, equity among public insurance users, and the examination of implicit and explicit biases toward African American males and publicly insured patients.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".