Correlates of keeping post-discharge appointments at a transitional care center: Implications for medical care disparities
Bibliographic record
Abstract
Objective: Over the past decade, transitional care (TC) programs have demonstrated initial benefits for decreased care costs, reduced rehospitalizations and emergency department visits via care coordination team activities. Patients who completed their TC follow-up appointment subsequently have less frequent ER visits. The current study addressed correlates of missed appointments in a sample of under-investigated, mostly vulnerable (e.g., middle-aged, uninsured) patients.Methods: We conducted a retrospective observational study of an appointment database for patients enrolled at a major transition center during the first three years since its establishment in Northern Florida. Patients (n = 2,146) were referred to a Transitional Care Center (TCC) from a regional medical center after discharge. The type of insurance and demographic characteristics of the patients was used to predict missed appointments.Results: Logistic regression analyses indicated that privately insured patients were more likely and Black publicly and privately insured patients were less likely to keep first appointments. No effect on keeping appointments was seen for uninsured patients.Conclusions: Our findings suggest that an appointment referral is a necessary but not sufficient step for the accomplishment of TC goals. Medical teams need to collaborate with other health professionals, such as social workers, to identify the barriers to keeping appointments and ensure effective solutions for achieving the goal of preventing future ER visits and rehospitalization.
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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 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".