Role of community health outreach program “living for health”® in improving access to federally qualified health centers in Miami-dade county, Florida: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Care of the underserved remains one of the most compelling challenges to American healthcare. Federally Qualified Health Centers (FQHCs) address uninsurance and underinsurance by providing primary and preventive care to vulnerable populations with fees charged based on ability to pay. Our goal is to study the effectiveness of FQHCs system in engaging patients and the barriers to utilization, which have not been well defined. METHODS: Retrospective analysis was performed on data from "Living for Health" (L4H) program participants from 2008 to 2012. Univariate and multivariate logistic regression analysis were performed to determine factors associated with FQHC utilization. RESULTS: Among 9453 subjects screened, 1889 were referred to a FQHC, but only 201(11%) actually sought treatment. Public insurance, non-Hispanic ethnicity, and hypertension were associated with higher rates of FQHC utilization. Inability to afford costs, cultural factors and inflexible appointment times were the most common reasons for FQHC underutilization. CONCLUSION: The current status of FQHC utilization is sub-optimal. Community outreach programs like L4H can improve the access and utilization of FQHCs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".