Practitioners' Views of Family Strengths: A Delphi Study

Reported is a research study to assess the opinions of family practitioners on the status of families in Oklahoma. Researchers employed the Delphi method to achieve consensus among key informants in the family practice field about the strengths and weaknesses of Oklahoma families, threats facing families in the state, and means to strengthening family life in Oklahoma. The study yielded qualitative data from the key informants, which the researchers then condensed into response categories to feed back to informants to rate. Family practitioners identified resilience, spirituality, and access to support systems as the greatest strengths, and listed substance abuse, poverty, and generational cycles of dysfunction as the greatest weaknesses of Oklahoma families. Recommendations by these practitioners are given for improvements in addressing family needs.

Commentary on "Practitioners’ Views of Family Strengths: A Delphi Study"

Commentary on "Practitioners’ Views of Family Strengths: A Delphi Study"

Use of a modified delphi process for establishing occupational exposure limits in support of health surveillance for space travelers

On-orbit exposures can come from numerous factors related to the space environment as evidenced by almost 50 years of environmental samples collected for water analysis, air analysis, radiation analysis, and physiologic parameters. For astronauts and spaceflight participants the occupational exposures can be very different from those experienced by workers performing similar tasks in workplaces on Earth, because the duration of the exposure could be continuous for very long orbital, and eventually interplanetary, missions. The establishment of long-term exposure standards is vital to controlling the quality of the spacecraft environment over long periods. NASA often needs to update and revise its prior exposure standards (Spacecrafts Maximum Allowable Concentrations (SMACs)). Traditional standards-setting processes are often lengthy, so a more rapid method to review and establish standards would be a substantial advancement in this area. This project investigates use of the Delphi method for this purpose. ^ In order to achieve the objectives of this study a modified Delphi methodology was tested in three trials executed by doctoral students and a panel of experts in disciplines related to occupational safety and health. During each test/trial modifications were made to the methodology. Prior to submission of the Delphi Questionnaire to the panel of experts a pilot study/trial was conducted using five doctoral students with the goals of testing and adjusting the Delphi questionnaire to improve comprehension, work out any procedural issues and evaluate the effectiveness of the questionnaire in drawing the desired responses. The remainder of the study consisted of two trials of the Modified Delphi process using 6 chemicals that currently have the potential of causing occupational exposures to NASA astronauts or spaceflight participants. To assist in setting Occupational Exposure Limits (OEL), the expert panel was established consisting of experts from academia, government and industry. Evidence was collected and used to create close-ended questionnaires which were submitted to the Delphi panel of experts for the establishment of OEL values for three chemicals from the list of six originally selected (trial 1). Once the first Delphi trial was completed, adjustments were made to the Delphi questionnaires and the process above was repeated with the remaining 3 chemicals (trial 2). ^ Results indicate that experience in occupational safety and health and with OEL methodologies can have a positive effect in minimizing the time experts take in completing this process. Based on the results of the questionnaires and comparison of the results with the SMAC already established by NASA, we conclude that use of the Delphi methodology is appropriate for use in the decision-making process for the selection of OELs.^

Data Accuracy in Medical Record Abstraction

Clinical Research Data Quality Literature Review and Pooled Analysis We present a literature review and secondary analysis of data accuracy in clinical research and related secondary data uses. A total of 93 papers meeting our inclusion criteria were categorized according to the data processing methods. Quantitative data accuracy information was abstracted from the articles and pooled. Our analysis demonstrates that the accuracy associated with data processing methods varies widely, with error rates ranging from 2 errors per 10,000 files to 5019 errors per 10,000 fields. Medical record abstraction was associated with the highest error rates (70–5019 errors per 10,000 fields). Data entered and processed at healthcare facilities had comparable error rates to data processed at central data processing centers. Error rates for data processed with single entry in the presence of on-screen checks were comparable to double entered data. While data processing and cleaning methods may explain a significant amount of the variability in data accuracy, additional factors not resolvable here likely exist. Defining Data Quality for Clinical Research: A Concept Analysis Despite notable previous attempts by experts to define data quality, the concept remains ambiguous and subject to the vagaries of natural language. This current lack of clarity continues to hamper research related to data quality issues. We present a formal concept analysis of data quality, which builds on and synthesizes previously published work. We further posit that discipline-level specificity may be required to achieve the desired definitional clarity. To this end, we combine work from the clinical research domain with findings from the general data quality literature to produce a discipline-specific definition and operationalization for data quality in clinical research. While the results are helpful to clinical research, the methodology of concept analysis may be useful in other fields to clarify data quality attributes and to achieve operational definitions. Medical Record Abstractor’s Perceptions of Factors Impacting the Accuracy of Abstracted Data Medical record abstraction (MRA) is known to be a significant source of data errors in secondary data uses. Factors impacting the accuracy of abstracted data are not reported consistently in the literature. Two Delphi processes were conducted with experienced medical record abstractors to assess abstractor’s perceptions about the factors. The Delphi process identified 9 factors that were not found in the literature, and differed with the literature by 5 factors in the top 25%. The Delphi results refuted seven factors reported in the literature as impacting the quality of abstracted data. The results provide insight into and indicate content validity of a significant number of the factors reported in the literature. Further, the results indicate general consistency between the perceptions of clinical research medical record abstractors and registry and quality improvement abstractors. Distributed Cognition Artifacts on Clinical Research Data Collection Forms Medical record abstraction, a primary mode of data collection in secondary data use, is associated with high error rates. Distributed cognition in medical record abstraction has not been studied as a possible explanation for abstraction errors. We employed the theory of distributed representation and representational analysis to systematically evaluate cognitive demands in medical record abstraction and the extent of external cognitive support employed in a sample of clinical research data collection forms. We show that the cognitive load required for abstraction in 61% of the sampled data elements was high, exceedingly so in 9%. Further, the data collection forms did not support external cognition for the most complex data elements. High working memory demands are a possible explanation for the association of data errors with data elements requiring abstractor interpretation, comparison, mapping or calculation. The representational analysis used here can be used to identify data elements with high cognitive demands.