Methodology
This independent project advocates for broader access to affordable healthcare in Texas. It examines Medicaid expansion through its effects on health, household finances, employment, rural communities and public budgets.
The project uses AI-assisted research and publishing, with linked sources, clearly identified uncertainties and a public correction process. Its purpose is to make the evidence understandable—including findings that complicate the case for expansion.
How claims are chosen
- Every factual claim links to a source. Each source is logged in an evidence table with its study design, period, population, effect size, limitations, conflicting evidence, and funder.
- Claims are triaged three ways. Supported claims are stated plainly. Qualified claims always carry their caveat. Omitted claims are not used, even when they would help the case. Omitted examples: "expansion reduces ER visits," "prevention saves money overall," and "expansion creates new businesses."
- Evidence labels: Strong design randomized or quasi-experimental; Association observational; Projection forecasts; Official data government statistics or records.
- Numbers carry dates. Laws and bills are described as of the "last reviewed" date on each page.
- Where possible, numbers were checked against the full text or the underlying data file. Some sources could be checked only at the abstract level; those claims are worded cautiously. Regional West Texas figures were computed by this project from federal data files (Census SAHIE 2024, CMS hospital directory, HRSA shortage-area file).
AI assistance
Research and drafting were AI-assisted. Errors are possible. Every claim links to its source so readers can check it, and corrections are logged publicly.
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