Main content

Home

Menu

Loading wiki pages...

View
Wiki Version:
**Principal Investigator(s):** **Matthew Grace** Hamilton College Email: [mgrace@hamilton.edu][1] Home page: [https://www.matthew-grace.com/][2] **Long Doan** University of Maryland Email: [longdoan@umd.edu][3] Home page: [https://www.longdoan.net/][4] **Sample size**: 5028 **Field period**: 07/31/2019-11/08/2019 **Abstract** Between one-fifth and a third of transgender people have been refused treatment by a medical provider due to their actual or perceived gender identity. New federal regulations provide clinicians with even greater discretion to deny medical treatment to members of this group on the basis of religious objections. Yet, we know little about the factors that shape public attitudes toward these issues. We present results from a nationally representative survey experiment (N = 4,887) that examines how common justifications issued by providers for the denial of healthcare, and the race and gender identity of the person being denied care, intersect to shape public opinion concerning the acceptability of treatment refusal. Across three outcomes, we examine the overall acceptability, redemptive acceptability (after a medical provider has offered alternative treatment paths), and believability of the doctor’s stated rationale. **Hypotheses** H1: Americans are less likely to endorse medical care refusal due to religious objections than insufficient medical training. H2: Americans are more likely to support treatment refusal to transmen than transwomen. H3: Americans are more likely to support treatment refusal to trans people of color than trans patients who are white. **Experimental Manipulations** We use a 2 (transman, transwoman) × 4 (black, white, Latinx, Asian) × 2 (inadequate training or religious objections) factorial experiment blocked by whether respondents work in the medical field. **Outcomes** To what extent do you agree or disagree that Dr. Smith should be [allowed to refuse/required] to treat [Name]? Why do you [strongly disagree/somewhat disagree/somewhat agree/strongly agree] that Dr. Smith should be [allowed to refuse/required] to treat [name]? In your own words, please write a few sentences explaining why you feel this way. To what extent do you agree that [inadequate training/religious objections] is the primary reason why Dr. Smith will not treat [name]? Suppose that Dr. Smith offered to refer [name] to another provider who can see [name] in the next day. To what extent do you agree or disagree that Dr. Smith should be [allowed to refuse/required] to treat [name]? Response categories: 1=Strongly Disagree, 2=Somewhat Disagree, 3=Somewhat Agree, 4=Strongly Agree **Summary of Results** We find that religious objections are viewed as less acceptable across each outcome compared to a medical justification, in this case, inadequate training. However, the difference between religious objections and inadequate training is larger when the person being denied healthcare is white or Asian than when they are Black or Latinx. There are few differences between transmen and transwomen. **References** Doan, Long, and Matthew K. Grace. 2020. “Factors Affecting Public Opinion on Denial of Healthcare to Transgender Persons.” Presented at the American Sociological Association Virtual Meeting. Grace, Matthew K., and Long Doan. nd. “Healthcare Professionals and Lay Persons’ Attitudes Toward Denial of Healthcare to Transgender Persons.” Working Paper. [1]: mailto:mgrace@hamilton.edu [2]: https://www.matthew-grace.com/ [3]: mailto:longdoan@umd.edu [4]: https://www.longdoan.net/
OSF does not support the use of Internet Explorer. For optimal performance, please switch to another browser.
Accept
This website relies on cookies to help provide a better user experience. By clicking Accept or continuing to use the site, you agree. For more information, see our Privacy Policy and information on cookie use.
Accept
×

Start managing your projects on the OSF today.

Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.