A Discriminant Analysis of Factors Influencing Mental Health levels among Elderly in Wangsomboon District, Sakaeo Province

Main Article Content

Rattanachai Pechsombut
Jumnion Suwannachart
Pasakorn Watanapruk
Piyathip Pradujprom
Kanok Phanthong

Abstract

Background: Thailand is an aging society, resulting is increased dependency. A decrease in self-esteem and changes in values for the elderly have been observed in Thai society which affect lifestyle and good mental health.


Objectives: To study the mental health level, analyze, classify factors influencing mental health levels, and to create equations to classify factors that influence mental health levels for the elderly.


Materials and methods: This research was study of predictive research. The sample consisted of 701 elderly people in Wangsomboon district by stratified random sampling. The instrument was an interview form for the factors influencing the mental health level of the elderly in Wangsombun district Sakaeo province. The statistical analysis was frequency, percentage, mean, standard deviation and discriminant analysis.


Results: 38.7% of the elderly had a level of mental health equal to that of the general public. 37.7% of the elderly had a level of mental health greater than that of the general public and 23.6% of the elderly had a level of mental health lower than that of the general public. Furthermore, the participation of the elderly club members and the activities of daily living (ADL) scores were factors that could classify the levels of mental health in the elderly. Three median values could predict a group of elderly with mental health level higher than general public by 47.8%. These values could create an equation of standard score showed as follow:  Zy = - 0.444 (participation of the elderly club members) + 0.754 (ADL scores)


Conclusion: The participation of the elderly club members and the activities of daily living (ADL) scores were factors that could classify the levels of mental health in the elderly. These two variables could predict the levels of mental health in the elderly at highest level.

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Original Article

References

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