Katherine R. B. Jankowski

Testing the Efficacy of Chaplaincy Care

The current article reviews the research conducted in the United States on the clinical practice of chaplains with patients and family members, referrals to chaplains, patient satisfaction with chaplaincy services, and the limited literature on the efficacy of chaplain interventions. It also discusses the methodological limitations of studies conducted on these topics and makes suggestions for improving future chaplaincy research. The authors conclude that past studies have not adequately defined chaplain interventions, nor sufficiently documented the clinical practice of chaplains, and that more and better designed studies are needed to test the efficacy of chaplaincy interventions. The authors recommend that chaplains generate research-based definitions of spirituality, spiritual care, and chaplaincy practice; and that more research be conducted to describe the unique contributions of chaplains to spiritual care, identify best chaplaincy practices to optimize patient and family health outcomes, and test the efficacy of chaplaincy care.

A Methodological Analysis of Chaplaincy Research: 2000–2009

The present article presents a comprehensive review and analysis of quantitative research conducted in the United States on chaplaincy and closely related topics published between 2000 and 2009. A combined search strategy identified 49 quantitative studies in 13 journals. The analysis focuses on the methodological sophistication of the studies, compared to earlier research on chaplaincy and pastoral care. Cross-sectional surveys of convenience samples still dominate the field, but sample sizes have increased somewhat over the past three decades. Reporting of the validity and reliability of measures continues to be low, although reporting of response rates has improved. Improvements in the use of inferential statistics and statistical controls were also observed, compared to previous research. The authors conclude that more experimental research is needed on chaplaincy, along with an increased use of hypothesis testing, regardless of the research designs that are used.

Change in Pastoral Skills, Emotional Intelligence, Self-reflection, and Social Desirability Across a Unit of CPE

Clinical Pastoral Education (CPE) is experiential learning of the art and science of pastoral care. CPE students increase capacities in psychological strengths such as self-awareness and empathy, and improve skills in interpersonal and inter-professional relationships. Previous research has documented different kinds of change and experiences for students depending on the length of the CPE unit (short/intensive vs. long/extended). The current study involves CPE students who have taken either an intensive unit or an extended unit and compares the average amount of change in pastoral skills, emotional intelligence, self-reflection, and insight while controlling for the influence of social desirability. Intensive and extended unit students were different from each other in terms of age and experience, beginning level of pastoral skill, and amount of change in the measured abilities. Limitations and strengths of this study and the need for future research are discussed.

Spiritual Coping and Anxiety in Palliative Care Patients: A Pilot Study

Patients often rely on spirituality to cope with anxiety, yet it is not known if spiritual coping actually helps patients deal with anxiety. The present study was designed, therefore, to examine this relationship. A series of patients who were referred to the palliative care team at New York University, Langone Medical Center (N = 44) were interviewed about their spiritual coping and anxiety. Anxiety was measured using the first three items of the GAD-7. Fourteen items, which were adapted from existing scales, were used to create the “Beliefs and Activities Spirituality Scale” (BASS), having two subscales: Activities (α = .79) and Beliefs (α = .82). Anxiety had a significant negative correlations with the total BASS (r = −.56), and the Activities (r = −.52) and Beliefs (r = −.42) subscales. The salubrious association of spiritual coping and anxiety remained for the BASS and the Activities subscale, after controlling for demographic variables.

Fundamentals of Measurement in Health Care Research

This article discusses levels of measurement and their application to research and practice in health care. The concept of levels of measurement was codified in a seminal article by S. S. Stevens in 1946 that defined four levels of measurement: nominal scales, which label and classify cases (objects and individuals) and assign them to categories; ordinal scales, which rank cases on some attribute; interval scales, which have equal intervals for measuring attributes; and ratio scales, which have equal intervals and a natural zero point. The rules that apply to each level of measurement are presented and the mathematical operations that can be performed on them are explained. The similarities and differences among the four types of scales are discussed and examples of their use in health care and other contexts are described.

Independent, Dependent, and Other Variables in Healthcare and Chaplaincy Research

This article begins by defining the term variable and the terms independent variable and dependent variable, providing examples of each. It then proceeds to describe and discuss synonyms for the terms independent variable and dependent variable, including treatment, intervention, predictor, and risk factor, and synonyms for dependent variable, such as response variables and outcomes. The article explains that the terms extraneous, nuisance, and confounding variables refer to any variable that can interfere with the ability to establish relationships between independent variables and dependent variables, and it describes ways to control for such confounds. It further explains that even though intervening, mediating, and moderating variables explicitly alter the relationship between independent variables and dependent variables, they help to explain the causal relationship between them. In addition, the article links terminology about variables with the concept of levels of measurement in research.

Research Designs and Making Causal Inferences From Health Care Studies

This article summarizes the major types of research designs used in healthcare research, including experimental, quasi-experimental, and observational studies. Observational studies are divided into survey studies (descriptive and correlational studies), case-studies and analytic studies, the last of which are commonly used in epidemiology: case-control, retrospective cohort, and prospective cohort studies. Similarities and differences among the research designs are described and the relative strength of evidence they provide is discussed. Emphasis is placed on five criteria for drawing causal inferences that are derived from the writings of the philosopher John Stuart Mill, especially his methods or canons. The application of the criteria to experimentation is explained. Particular attention is given to the degree to which different designs meet the five criteria for making causal inferences. Examples of specific studies that have used various designs in chaplaincy research are provided.

2009 Spiritual Care Collaborative Survey Results on Continuing Education

Continuing education for chaplains, pastoral counselors and clinical pastoral educators is important for maintaining and advancing professional competency. Pastoral professionals who visited the Spiritual Care Collaborative (SCC) website to register for a conference were asked to complete a questionnaire on continuing education. Results of the survey show that continuing education, both in-person and through electronic means, were clearly ranked as the most important activities the SCC partner organizations could provide in the future. Additionally, continuing education preferences vary depending upon constituent groups. These findings have implications for the design of future educational programs.

Studying Associations in Health Care Research

This article discusses some of the types of relationships observed in healthcare research and depicts them in graphic form. The article begins by explaining two basic associations observed in chemistry and physics (Boyles’ Law and Charles’ Law), and illustrates how these associations are similar to curvilinear and linear associations, respectively, found in healthcare. Graphs of curvilinear associations include morbidity curves and survival and mortality curves. Several examples of linear relationships are given and methods of testing linear relationships with interval and ratio data are introduced (i.e., correlation and ordinary least-squares regression). In addition, 2 × 2 contingency tables for testing the association between categorical (or nominal) data are described. Finally, Sir Austin Bradford Hill’s eight criteria for assessing causality from research on associations between variables are presented and explained. Three appendices provide interested readers with opportunities to practice interpreting selected curvilinear and linear relationships.

Measures of Variability in Chaplaincy, Health Care, and Related Research

This article discusses statistical measures of variability in relation to measures of central tendency and levels of measurement. Three measures of variability used in healthcare research (the range, the interquartile range, and the standard deviation) are described and compared, including their uses and limitations. The article describes how each of the three measures is calculated, and it provides a step-by-step example of calculating the sums of squares, variance, and standard deviation. Graphs of frequency and percentage distributions are used to show how the interquartile range and the standard deviation represent the variability observed within distributions. The article discusses the properties of the normal curve regarding the distribution of scores around the mean in relation to the standard deviation, and illustrates differences in the shapes of normal curves with the same mean but different standard deviations.