Understanding Financial Barriers to Care in Patients With Diabetes
Bibliographic record
Abstract
Purpose The purpose of this study was to better understand the impact that financial barriers have on patients with diabetes and the strategies that they use to cope with them. Methods A secondary analysis was conducted of 24 interviews with patients who had either type 1 or type 2 diabetes and perceived financial barriers, which were previously undertaken for a larger grounded theory study. Semistructured interviews were undertaken either face-to-face or by telephone. Data analysis was performed by 3 reviewers using inductive thematic analysis. Sampling for the original study continued until data saturation was achieved. Results The predominant aspects of care to which participants described financial barriers were medications, diabetes supplies, and healthy food. A variety of strategies are used by these patients. Participants described that their health care providers had the potential to either play an important supporting role; or alternatively, that they could also worsen the impacts of financial barriers. Conclusions Patients with diabetes experience financial barriers to various aspects of their care. While they use a variety of strategies to overcome their barriers, their health care providers can play a particularly important role in helping them manage these important barriers that impact their care and outcomes. Providers should ask patients about the existence of financial barriers, and employ strategies to mitigate against their impact.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".