Efficiency of Protection Motivation Theory on Prediction of Skin Cancer and Sunlight Preventive Behaviors in Farmers in Ilam County
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J Tazval * , M Ghafari , F Mohtashami Yeganeh , T Babazadeh , R Rabati  |
Ilam University of Medical Sciences |
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Abstract: (7033 Views) |
Background & objectives: Skin cancer is one of the most prevalent cancers in Iran and worldwide. According to statistics, skin cancer is more prevalent in Ilam. The purpose of this study was to predict the skin cancer and sunlight preventive behaviors in farmers of the Ilam County by using the protection motivation theory in 2013.
Methods: In a cross-sectional study samples were collected by multistage random sampling method. The required data was collected by a researcher-made questionnaire from 248 farmers in rural areas of Ilam city in May 2013. The questions were based on demographic variables, protection motivation theory and skin cancer preventive behaviors. Data analysis was done by descriptive and analytical statistics using SPSS software version 16 .
Results: Correlation coefficient showed that skin cancer preventive behaviors positively correlated with perceived susceptibility, perceived severity, response efficacy, and self-efficacy and negatively correlated with internal and external rewards and response costs. The protection motivation theory structures predicted 73 percent of the variance for protection motivation. Self-efficacy was the strongest predictor for motivation protection (β=0.420). Protection motivation was predicted 77 percent of the variance for skin cancer preventive behaviors.
Conclusion: According to the obtained results, protection motivation theory is an appropriate framework for designing and implementation of educational interventions. Since, self-efficacy was strongest predictor for motivation protection; therefore, use of this framework is recommended for similar studies. |
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Keywords: Predictors of Skin Cancer, Protection Motivation Theory, Farmers. |
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Full-Text [PDF 173 kb]
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Type of Study: Research |
Subject:
General Received: 2016/12/11 | Accepted: 2016/12/11 | Published: 2016/12/11
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