Medical Mistrust Among Food Insecure Individuals in Appalachia
Bibliographic record
Abstract
This study focused on the relationship between food insecurity and medical mistrust within Appalachia. Food insecurity has negative consequences on health, while medical mistrust can lead to a decrease in health care use, creating additive consequences to already vulnerable populations. Medical mistrust has been defined in various ways, with measures addressing health care organizations and individual health care providers. To determine whether food insecurity has an additive impact on medical mistrust, a cross-sectional survey was completed by 248 residents in Appalachia Ohio while attending community or mobile clinics, food banks, or the county health department. More than one-quarter of the respondents had high levels of mistrust toward health care organizations. Those with high food insecurity levels were more likely to have higher levels of medical mistrust than those with lower levels of food insecurity. Individuals with higher self-identified health issues and older participants had higher medical mistrust scores. Screening for food insecurity in primary care can reduce the impact of mistrust on patient adherence and health care access by increasing patient-centered communication. These findings present a unique perspective on how to identify and mitigate medical mistrust within Appalachia and call attention to the need for further research on the root causes among food insecure residents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".