Compliance With Routine Health Checkup Visits Among California-Based Minority Men: A Survey Study
Bibliographic record
Abstract
The literature on health care disparities among U.S. minority men remains limited, and post-pandemic changes in the health care delivery system may uniquely affect this population. We assessed the factors influencing California-based minority men's compliance with routine health checkup. An IRB-approved survey was conducted electronically by convenience sampling between October 2022 and July 2023. Data was collected on demographics, socioeconomic status, health insurance, and routine checkup attendance. Health insurance literacy was assessed by self-reported ability to locate insurance-covered clinics and health care information. The data was analyzed using random forest modeling with both feature importance and SHAP values for interpretability, and logistic regression analysis. A total of 266 male respondents participated. Of these, 60.5% were under 30 years old, and 66.9% identified as Latino/Hispanic.The majority were employed (82.7%), insured (84.9%), and earned less than $50,000 annually (64.5%). While 71.8% were connected to a clinic or hospital, only 50.8% attended routine health checkup, and 6.8% had visited a doctor in the past year. Key factors influencing compliance included zip code, connection to a clinic and the ability to locate a clinic covered by insurance. These findings highlight that half of insured minority men in California under 60 years of age are not attending routine checkups, suggesting significant barriers related to accessibility and health insurance literacy. Addressing these disparities could improve health care utilization and outcomes in this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".