“I Had Bills to Pay”: a Mixed-Methods Study on the Role of Income on Care Transitions in a Public-Payer Healthcare System
Bibliographic record
Abstract
BACKGROUND: Income disparities may affect patients' care transition home. Evidence among patients who have access to publicly funded healthcare coverage remains limited. OBJECTIVE: To evaluate the association between low income and post-discharge health outcomes and explore patient and caregiver perspectives on the role of income disparities. DESIGN: Mixed-methods secondary analysis conducted among participants in a double-blind randomized controlled trial. PARTICIPANTS: Participants from a multicenter study in Ontario, Canada, were classified as low income if annual self-reported salary was below $29,000 CAD, or between $30,000 and $50,000 CAD and supported ≥ 3 individuals. MAIN MEASURES: The associations between low income and the following self-reported outcomes were evaluated using multivariable logistic regression: patient experience, adherence to medications, diet, activity and follow-up, and the aggregate of emergency department (ED) visits, readmission, or death up to 3 months post-discharge. A deductive direct content analysis of patient and caregivers on the role of income-related disparities during care transitions was conducted. KEY RESULTS: Individuals had similar odds of reporting high patient experience and adherence to instructions regardless of reported income. Compared to higher income individuals, low-income individuals also had similar odds of ED visits, readmissions, and death within 3 months post-discharge. Low-income individuals were more likely than high-income individuals to report understanding their medications completely (OR 1.9, 95% CI: 1.0-3.4) in fully adjusted regression models. Two themes emerged from 25 interviews which (1) highlight constraints of publicly funded services and costs incurred to patients or their caregivers along with (2) the various ways patients adapt through caregiver support, private services, or prioritizing finances over health. CONCLUSIONS: There were few quantitative differences in patient experience, adherence, ED visits, readmissions, and death post-discharge between individuals reporting low versus higher income. Several hidden costs for transportation, medications, and home care were reported however and warrant further research.
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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 | 0.036 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".