R. Reeb, Nyssa L Snow-Hill, Susan F. Folger, Anne L. Steel, L. Stayton, C. A. Hunt, Bernadette D. O'Koon, Zachary Glendening
{"title":"Psycho-Ecological Systems Model: A Systems Approach to Planning and Gauging the Community Impact of Community-Engaged Scholarship","authors":"R. Reeb, Nyssa L Snow-Hill, Susan F. Folger, Anne L. Steel, L. Stayton, C. A. Hunt, Bernadette D. O'Koon, Zachary Glendening","doi":"10.3998/mjcsloa.3239521.0024.102","DOIUrl":null,"url":null,"abstract":"This article presents the PsychoEcological Systems Model (PESM) – an integrative conceptual model rooted in General Systems Theory (GST). PESM was developed to inform and guide the development, implementation, and evaluation of transdisciplinary (and multilevel) communityengaged scholarship (e.g., a participatory community action research project undertaken by faculty that involves graduate and/or undergraduate students as servicelearning research assistants). To set the stage, the first section critiques past conceptual models. Following a description of GST, the second section provides a comprehensive description of PESM, which represents an integration of three conceptual developments: the ecological systems model (Bronfenbrenner, 1979), the biopsychosocial model (Kiesler, 2000), and the principle of reciprocal determinism (Bandura, 1978). In the third section, we discuss implications of PESM for communitybased research. A greater emphasis on the development of integrative conceptual frameworks may increase the likelihood that communitybased research projects will: (a) address complex questions; (b) develop and implement efficacious (and sustainable) transdisciplinary (and multilevel) projects; (c) assess constructs at multiple levels using a blend of quantitative and qualitative approaches; and (d) utilize multiple research designs and methods to systematically examine hypotheses regarding a project’s influence on outcome variables and process variables.","PeriodicalId":93128,"journal":{"name":"Michigan journal of community service learning","volume":"535 1","pages":"6-22"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Michigan journal of community service learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3998/mjcsloa.3239521.0024.102","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
This article presents the PsychoEcological Systems Model (PESM) – an integrative conceptual model rooted in General Systems Theory (GST). PESM was developed to inform and guide the development, implementation, and evaluation of transdisciplinary (and multilevel) communityengaged scholarship (e.g., a participatory community action research project undertaken by faculty that involves graduate and/or undergraduate students as servicelearning research assistants). To set the stage, the first section critiques past conceptual models. Following a description of GST, the second section provides a comprehensive description of PESM, which represents an integration of three conceptual developments: the ecological systems model (Bronfenbrenner, 1979), the biopsychosocial model (Kiesler, 2000), and the principle of reciprocal determinism (Bandura, 1978). In the third section, we discuss implications of PESM for communitybased research. A greater emphasis on the development of integrative conceptual frameworks may increase the likelihood that communitybased research projects will: (a) address complex questions; (b) develop and implement efficacious (and sustainable) transdisciplinary (and multilevel) projects; (c) assess constructs at multiple levels using a blend of quantitative and qualitative approaches; and (d) utilize multiple research designs and methods to systematically examine hypotheses regarding a project’s influence on outcome variables and process variables.