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Predicting Perinatal Low Mood and Depression for BAME Women - The Role of Treatment, Perceived Public, and Internalised Stigma

Elaine Amoah*

The study aims to build on existing empirical evidence looking at the extent to which stigma impacts perinatal low mood and depression. This study examined the associations between treatment, perceived public, and internalised stigma and perinatal low mood and symptoms of depression. The study aimed to examine perinatal depression symptoms and three predictor variables.

This study used correlational cross-sectional survey data from a representative sample of BAME (Black, Asian and Minority Ethnic) women (n=123). The BAME women completed four surveys to assess perinatal depression symptoms and treatment, perceived public, and internalised stigma toward perinatal depression symptoms conducted online. The results were subsequently uploaded onto SPSS. Exploratory research was also analysed to investigate preferable approaches to discuss low mood with a healthcare professional.

Results showed internalised stigma was predictive of perinatal depression symptoms for BAME women. Internalised stigma was found to be a significant predictor variable of perinatal depression for both women who identified as Black and those that identified as all other ethnic minority groups. In addition, for the BAME women with an upper income and from the 25-34 age group internalised stigma was found to be predictive of perinatal depression symptoms. Treatment stigma was also a significant predictor variable of perinatal depression for BAME women aged between 25-34. Perceived public stigma was found to be a significant predictor variable of perinatal depression symptoms for BAME women with a lower income.

Improvements on the present study, and implications for future research, are discussed with regard to the findings. These results suggest that, at least in this popula¬tion, internalised stigma is an important factor towards perinatal depression.

Отказ от ответственности: Этот реферат был переведен с помощью инструментов искусственного интеллекта и еще не прошел проверку или верификацию
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