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European journal of orthodontics v.39 no.1, 2017년, pp.85 - 91   SCI SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Determinants of orthodontic treatment need and demand: a cross-sectional path model study

Taghavi Bayat, Jari (<sup>1</sup>Division of Orthodontics, Department of Dental Medicine, Karolinska Institutet, Huddinge, Sweden, ) ; Huggare, Jan (<sup>1</sup>Division of Orthodontics, Department of Dental Medicine, Karolinska Institutet, Huddinge, Sweden, ) ; Mohlin, Bengt (<sup>2</sup>Department of Orthodontics, Institute of Odontology, Sahlgrenska Academy, University of Gothenburg, Sweden and ) ; Akrami, Nazar (<sup>3</sup>Department of Psychology, Uppsala University, Sweden ) ;
  • 초록  

    Objectives: To put forward a model predicting orthodontic treatment need and demand. Furthermore, to explore how much of the variance in treatment demand could be explained by a set of self-assessed measures, and how these measures relate to professionally assessed treatment need. Subjects and methods: One hundred and fifty adolescents, aged 13 years, completed a questionnaire which included a set of self-assessed measures dealing with self-esteem, such as dental and global self-esteem, various aspects of malocclusion, such as perceived malocclusion and perceived functional limitation, and treatment demand. Treatment need was assessed by Dental Health Component of the Index of Orthodontic Treatment Need grading. Path analysis was used to examine the relations between the measures and if they could predict treatment need and demand. Results: The measures proved to be reliable and inter-correlated. Path analysis revealed that the proposed model had good fit to the data, providing a test of the unique effect of all included measures on treatment need and demand. The model explained 33% of the variance in treatment demand and 22% of the variance in treatment need. Limitations: The specific age group could affect the generalizability of the findings. Moreover, although showing good fit to data, the final model is based on a combination of theoretical reasoning and semi-explorative approach. Conclusions: The proposed model displays the unique effect of each included measure on treatment need and demand, explaining a large proportion of the variance in perceived treatment demand and professionally assessed treatment need. The model would hopefully lead to improved and more cost-efficient predictions of treatment need and demand.


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