The effects of a community-based partnership, Project Access Dallas (PAD), on emergency department utilization and costs among the uninsured
Bibliographic record
Abstract
BACKGROUND: Approximately 19% of non-elderly adults are without health insurance. The uninsured frequently lack a source of primary care and are more likely to use the emergency department (ED) for routine care. Improving access to primary care for the uninsured is one strategy to reduce ED overutilization and related costs. METHODS: A comparison group quasi-experimental design was used to evaluate a broad-based community partnership that provided access to care for the uninsured-Project Access Dallas (PAD)-on ED utilization and related costs. Eligible uninsured patients seen in the ED were enrolled in PAD (n = 265) with similar patients not enrolled in PAD (n = 309) serving as controls. Study patients were aged 18-65 years, <200% of the federal poverty level and uninsured. Outcome measures include the number of ED visits, hospital days and direct and indirect costs. RESULTS: PAD program enrollees had significantly fewer ED visits (0.93 vs. 1.44; P < 0.01) and fewer inpatient hospital days (0.37 vs. 1.07; P < 0.05) than controls. Direct hospital costs were ∼60% less ($1188 vs. $446; P < 0.01) and indirect costs were 50% less ($313 vs. $692; P < 0.01). CONCLUSIONS: A broad-based community partnership program can significantly reduce ED utilization and related costs among the uninsured.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.003 |
| 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".