An Inhospitable World: Exploring a Model of Objectification Theory With Trans Women.
Allison ComiskeyMike C ParentElliot A TebbePublished in: Psychology of women quarterly (2019)
In this study, we investigated key tenets of objectification theory, a prominent model of body image disturbance, as it relates to trans women's disordered eating and intention to obtain silicone injections-a specific health risk for this population. We also incorporated appearance congruence, or the degree to which an individual personally feels that their gender expression matches their gender identity, into the objectification theory model. Results of a structural equation model using data from a sample of 173 trans women from the United States indicated that the basic objectification theory model held among this sample and that appearance congruence was associated negatively with body surveillance. However, appearance congruence did not have significant direct or indirect links (via body surveillance and body shame) with disordered eating or intention to obtain silicone injections. Thus, disordered eating and intention to obtain silicone injections are potential negative outcomes of the process of objectification among trans women, and appearance congruence does not appear to be uniquely linked to health risks associated with internalization of cultural standards of attractiveness, body surveillance, and body shame. Our findings support the application of the tenets of objectification theory with trans women as they apply to disordered eating and intention to obtain silicone injections and also indicate the need to identify other positive influences on trans women's body image to counteract internalization of cultural standards of attractiveness.
Keyphrases
- polycystic ovary syndrome
- pregnancy outcomes
- public health
- physical activity
- health risk
- weight loss
- breast cancer risk
- platelet rich plasma
- ultrasound guided
- mental health
- poor prognosis
- type diabetes
- skeletal muscle
- risk assessment
- big data
- mass spectrometry
- heavy metals
- high resolution
- machine learning
- deep learning
- climate change
- glycemic control