Accommodations for Patients with Disabilities in Primary Care: A Mixed Methods Study of Practice Administrators
Bibliographic record
Abstract
Structural barriers that limit access to health care services for people with disabilities have been identified through qualitative studies; however, little is known about how patients with disabilities are accommodated in the clinical setting when a structural barrier is encountered. The purpose of this study was to identify how primary care medical practices in the United States accommodated people with disabilities when a barrier to service is encountered. Primary care practice administrators from the medical management organization were identified through the organization's website. Sixty-three administrators from across the US participated in this study. Practice administrators reported that patients were examined in their wheelchairs (76%), that parts of the exam where skipped when a barrier was encountered (44%), that patients were asked to bring someone with them (52.4%) or that patients were refused treatment due to an inaccessible clinic (3.2%). These methods of accommodation would not be in compliance with requirements of the Americans with Disabilities Act. There was not a significant difference (p>0.05) in accommodations for patients with disabilities between administrators who could describe the application of the ADA to their clinic and those who could not. Practice administrators need a comprehensive understanding of the array of challenges encountered by patients with disabilities throughout the health care process and of how to best accommodate patients with disabilities in their practice.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".