Collection of Patients' Disability Status by Healthcare Organizations: Patients' Perceptions and Attitudes
Bibliographic record
Abstract
Recent policies call for healthcare organizations to consistently document patients' disability status for the purpose of tracking the quality of care experienced by patients with disabilities. The purpose of the study was to explore patients' attitudes toward healthcare organizations collecting disability status. We surveyed a convenience sample of patients in three outpatient clinics, including primary care and rehabilitation clinics. A total of 303 patients participated; 49% self-identified as disabled, 59% were female and the mean age was 52 years. The majority of participants (88%) either agreed or strongly agreed that it is important for healthcare organizations to collect information about disabilities; 77% stated that they were comfortable or very comfortable with healthcare organizations collecting this information. By contrast, we found that almost a quarter of participants had concerns with front desk staff collecting disability status information. When we presented disability questions endorsed by the Health and Human Services Department, over a quarter of participants (28%) felt that the questions were not inclusive of all disability categories. Although patients are supportive of healthcare organizations collecting disability status information, concerns exist regarding how the information is collected and which categories are included, suggesting the need for continued development of evidence-based, patient-centered methods and questions.
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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 | 0.005 | 0.015 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".