An Adapted Model of Cost-Related Nonadherence to Medications Among People With Disabilities
Bibliographic record
Abstract
Despite emerging evidence on cost-related nonadherence (CRNA) to prescription medications, there is little conceptualization and exploration of this phenomenon with respect to disability. Specifically, there is a gap in the literature that explores factors influencing medication cost–adherence relationship among individuals living with a disability. To advance research on and policy for CRNA to medications among people with disabilities, we need a framework that can contribute towards guiding solutions to this problem. We examined the applicability of Piette and colleagues’ existing model for CRNA to the context of people with disabilities and suggested an adapted model (CRNA to medications for persons with disability [CRNA-d]) that can provide a more specific conceptualization of CRNA with respect to disability. The adapted CRNA-d model depicts that CRNA to prescription medications with respect to disability is a dynamic and multifaceted phenomenon, determined by various socioeconomic, disability-related, medication-related, prescriber-related, and system-related factors. We discuss how higher susceptibility to health complications, barriers to income and employment, additional health care costs, the complexity of medical regimens, limited access to physician services, and other policy-related factors increase the risk of persons with disabilities to face cost-related barriers to fulfill their necessary medications.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".