Perceived Disparities in Access to Health Care Due to Cost for Women with Disabilities
Bibliographic record
Abstract
Individuals with disabilities tend to have more health care access problems, have secondary health conditions, have unmet health care needs, and are less likely to be satisfied with medical care than those without disabilities (Batavia, 1995; Hagglund, Clark, Conforti, & Shigaki, 1999). With nearly 20% of the US population with one or more disabilities, there is a great need to address the barriers that prevent this population from accessing the health care services, such as physician services, that they require (US Bureau of Census, 2006). Scheer, Kroll, Neri and Beatty (2003) defined three broad categories of barriers to health care services: environmental, structural, and process. Environmental barriers include issues of office accessibility, such as parking, entry, restrooms, waiting rooms, examination rooms, and diagnostic equipment. Physician's offices have been noted to lack equipment and space essential for treatment of patients using wheelchairs. Many offices are without adequate room to maneuver a wheelchair, use examination tables that are too high, and do not have sit-in scales (DeJong, 1997). Transportation barriers such as the lack of access to public transportation, publicly funded door-to door transportation, and taxicab services, also have an effect on the perceived accessibility of health care services (Scheer et al., 2003). Structural barriers refer to a lack of financial resources for necessary services. Process barriers relate to the delivery of service. For example, lack of provider knowledge and lack of timeliness of service are issues frequently reported by patients. Provider attitudes have also been cited as a barrier to accessing services (Au & Man, 2006) Cost as a Barrier to Health Care Access Addressing structural barriers specifically, Sheer and colleagues (2003) argue that limited health benefit programs, whether publicly or privately funded, may not provide for services such as physical and occupational therapy, high quality and well-fitted functional durable medical equipment, and mental health services. In addition, a considerable proportion of people with disabilities report serious problems accessing prescription drugs (32%), dental care (29%), equipment (21%), mental health services (17%), and home care (16%) due to cost (Kennedy & Erb, 2002; Schultz, Shenkin, & Horowitz, 1998). Barriers to Physician Services for Women with Disabilities There is a limited but growing body of research focused on health care disparities faced by women with disabilities. According to a study by Parish and Huh (2006), women with disabilities experience poorer health and preventive care than women without disabilities. Results of this study revealed that 19 percent of women with disabilities postponed such care compared with the 8 percent of women without disabilities. Additionally, 20 percent of women with disabilities postponed getting needed medications compared to 6 percent of women without disabilities A study by Parish and Ellison-Martin (2007) examined a national sample of low-income female Medicaid recipients and found that despite having similar potential for care (i.e., health insurance, usual source of care, and having a physician as a usual source of care) compared to women without disabilities, women with disabilities had substantially worse rates of receiving medical care and medication when they were needed. In a study utilizing a national publicly available dataset, Smith (2008) found that women with disabilities had less access to health care than women without disabilities and men with disabilities. Chevarley, Theirry, Gill, Ryerson, and Nosek (2006) found that women with three or more functional limitations were more likely to report being unable to get general medical care, dental care, prescription medicines, or eyeglasses compared to women without functional limitations. The primary reason cited for being unable to receive general care were financial problems or limitations in insurance. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".