Application of Behavioral Analysis Phase of PRECEDE Model on Women’s Psychological Well-being in the Menopausal Period
Year: 2015
Type: article
Study design: Observational Study
Abstract: Background and Aims Positive psychology introduce mental health as a positive psychological functioning and represent it as wellbeing psychology and having positive characteristics such as self-esteem, positive social relationships, and life satisfaction. The purpose of this study was to investigate the psychological wellbeing of postmenopausal women based on the behavioral analysis phase of PRECEDE model. Materials and Methods This study is a cross-sectional survey and carried out through random sampling in the case of 110 postmenopausal women at Ferdows city, Iran in 2014. The psychological wellbeing variable was studied by Ryff test, including 84 questions and six components containing autonomy, purposeful in life, dominance on environment, personal development, positive relationships with others, and self-acceptance. The collected data were analyzed by SPSS v.20 using descriptive statistics and analytic statistics through correlation and ANOVA. Results The mean age of participants and the average number of their children were 51.62 ± 4.26 and 3.42 ± 1.38, respectively. 61.80 percent of participants had primary education. Pearson correlation test indicated a positive and significant relationship between psychological wellbeing and predisposing factors (knowledge, attitude toward menopause), environmentalbehavioral factors, enabling factors and reinforcing factors (P < 0.01). Conclusion The results indicated that increasing awareness, creating positive attitude and promoting healthy behaviors are the effective steps to improve the psychological wellbeing in postmenopausal women. * Corresponding Author: Gonabad University of Medical Sciences, Faculty of Public Health. Email: [email protected]
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2528893844