Access to Health Services: Experiences of Women with Neurological Disabilities
Bibliographic record
Abstract
Health is essential to the quality of life of an individual and consists of three domains: physical, psychological and social (Krotoski, Nosek, & Turk, 1996). The physical domain refers to the wellbeing in an individual's body. The psychological domain refers to wellbeing in a person's mind and emotions. And, the social domain refers to an individual's satisfaction with one's social relationships and participation in social activities. People with disabilities often encountered challenges to maintaining overall due to society's physical and attitudinal barriers (Donnelly et al., 2007; Neri & Kroll, 2003; Nosek, 2000; Putnam et al., 2003; Scherer & Dicowden, 2008; Shigaki, Hagglund, Clark, & Conforti, 2002). Several national and statewide surveys indicated that people with disabilities had limited access to healthcare compared to people without disabilities (Chevarley, Thierry, Gill, Ryerson, & Nosek, 2006; Hagglund Clark, Conforti, & Shigaki, 1999; Minden, Frankel, Hadden, & Hoaglin, 2007). This lack of access to primary care then led to recurrent, but often preventable, problems which, in turn, affected education, employment, and quality of life of people with disabilities (Iezzoni, Davis, Soukup, & O'Day, 2002; Nosek, 2000; Shigaki et al., 2002; Thierry & Cyril, 2004; US Department of Health & Human Services, 2000). Minden et al. (2007) examined the data obtained from the Sonya Slitka Longitudinal Multiple Sclerosis Study. They reported that substantial numbers of people with multiple sclerosis (MS) had (a) limited access to specialists, (b) restricted choice of hospitals/providers, and (c) problems obtaining mental services. Shigaki et al., (2002) studied access to healthcare among people with rehabilitation needs receiving Medicaid in Missouri. They found that individuals with spinal cord injuries (SCI) reported the most frequent difficulties accessing healthcare services. The National Healthcare Disparities Report of 2005 (U.S. Department of Health and Human Services, 2005) indicates that are observed across many clinical conditions including mental health and subpopulations including ethnic minorities, women, and individuals with According to the Bureau of the Census (2008), approximately 1 in 5 women had a disability in the U.S. in 2002, and American women have a higher disability incident rate compared to American men. Despite the number of women with disabilities there have been limited studies on access to healthcare among women with disabilities. A few national investigations indicated that women with disabilities were not well served by the healthcare and rehabilitation systems compared to women without disabilities (Chevarley et al, 2006; Hughes, 2006; Parish & Huh, 2006; Parish & Ellison-Martin, 2007; Nosek, Howland, Rintala, & Young, 2001; Smith, 2008; Smith & Ruiz, 2009). Based on an analysis of the 2004 data from the Behavioral Risk Factor Surveillance System, Smith and Ruiz (2009) reported that women with disabilities experienced disparities more than women without disabilities and men with disabilities in that they were less likely to see a doctor due to cost. Through longitudinal analyses of 1999-2002 Medical Expenditure Survey, Wei, Findley, and Sambamoorthi (2006) found that women with disabilities were less likely to receive mammography and Pap smears within the recommended intervals compared to women without disabilities. Murphy, Molnar, and Lankasky (1995) surveyed 101 adults with cerebral palsy in San Francisco and found that 90% of women with cerebral palsy in their sample did not have regular Pap smears, pelvic examinations, or breast examinations. In a survey study of preventive healthcare among 220 women with MS, Shabas and Weinrebs (2000) found that 50% of the women did not get regular medical preventive checkups. Nosek et al. (2001) indicated that routine screening for breast cancer with mammography was difficult for many women in wheelchairs because of the inaccessibility of the facility (e. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| 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) | 0.002 | 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 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".