A Machine Learning Approach to Evaluating Illness-Induced Religious Struggle.
Religious or spiritual struggles are clinically important to health care chaplains because they are related to poorer health outcomes, involving both mental and physical health problems. Identifying persons experiencing religious struggle poses a challenge for chaplains. One potentially underappreci...
| Publicado en: | Biomedical Informatics Insights no. 9; pp. 1 - 10 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=123816983&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 123816983 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11782226 B077 jtl: Biomedical Informatics Insights issn: 11782226 maglogo: Y pubinfo: dt: 2017 iid: 9 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 123816983 123816983 123816983 10.1177/1178222616686067 123816983 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: A Machine Learning Approach to Evaluating Illness-Induced Religious Struggle. aug: au: Glauser, Joshua Connolly, Brian Nash, Paul Grossoehme, Daniel H. affil: Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA sug: subj: Chaplains Prayer Judaism Machinery Mental Health Religion and Psychology Natural Language Processing Learning Data Analysis, Statistical Structural Equation Modeling Reliability and Validity Interrater Reliability Prospective Studies Construct Validity Content Analysis Psychological Tests Empirical Research Analysis of Variance Data Collection Scales Probability Surveys Delphi Technique Questionnaires Reliability Regression Validity Sensitivity and Specificity ab: Religious or spiritual struggles are clinically important to health care chaplains because they are related to poorer health outcomes, involving both mental and physical health problems. Identifying persons experiencing religious struggle poses a challenge for chaplains. One potentially underappreciated means of triaging chaplaincy effort are prayers written in chapel notebooks. We show that religious struggle can be identified in these notebooks through instances of negative religious coping, such as feeling anger or abandonment toward God. We built a data set of entries in chapel notebooks and classified them as showing religious struggle, or not. We show that natural language processing techniques can be used to automatically classify the entries with respect to whether or not they reflect religious struggle with as much accuracy as humans. The work has potential applications to triaging chapel notebook entries for further attention from pastoral care staff. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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