scholarly journals Results of the Behavioral Health Readiness Evaluation and Decision-Making Instrument Study

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 142-152
Author(s):  
Justin M Curley ◽  
Katie L Nugent ◽  
Kristina M Clarke-Walper ◽  
Elizabeth A Penix ◽  
James B Macdonald ◽  
...  
Keyword(s):  
Decision Making ◽  
Behavioral Health ◽  
User Satisfaction ◽  
Safety Issue ◽  
Walter Reed Army Institute ◽  
End User ◽  
Chi Square ◽  
Proficiency Level ◽  
Wide Range ◽  
Decision Making Support

ABSTRACT Introduction Recent reports have demonstrated behavioral health (BH) system and individual provider challenges to BH readiness success. These pose a risk to winning on the battlefield and present a significant safety issue for the Army. One of the most promising areas for achieving better BH readiness results lies in improving readiness decision-making support for BH providers. The Walter Reed Army Institute of Research (WRAIR) has taken the lead in addressing this challenge by developing and empirically testing such tools. The results of the Behavioral Health Readiness Evaluation and Decision-Making Instrument (B-REDI) field study are herein described. Methods The B-REDI study received WRAIR Institutional Review Board approval, and BH providers across five U.S. Army Forces Command installations completed surveys from September 2018 to March 2019. The B-REDI tools/training were disseminated to 307 providers through random clinic assignments. Of these, 250 (81%) providers consented to participate and 149 (60%) completed both initial and 3-month follow-up surveys. Survey items included a wide range of satisfaction, utilization, and proficiency-level outcome measures. Analyses included examinations of descriptive statistics, McNemar’s tests pre-/post-B-REDI exposure, Z-tests with subgroup populations, and chi-square tests with demographic comparisons. Results The B-REDI resulted in broad, statistically significant improvements across the measured range of provider proficiency-level outcomes. Net gains in each domain ranged from 16.5% to 22.9% for knowledge/awareness (P = .000), from 11.1% to 15.8% for personal confidence (P = .001-.000), and from 6.2% to 15.1% for decision-making/documentation (P = .035-.002) 3 months following B-REDI initiation, and only one (knowledge) failed to maintain a statistically significant improvement in all of its subcategories. The B-REDI also received high favorability ratings (79%-97% positive) across a wide array of end-user satisfaction measures. Conclusions The B-REDI directly addresses several critical Army BH readiness challenges by providing tangible decision-making support solutions for BH providers. Providers reported high degrees of end-user B-REDI satisfaction and significant improvements in all measured provider proficiency-level domains. By effectively addressing the readiness decision-making challenges Army BH providers encounter, B-REDI provides the Army BH health care system with a successful blueprint to set the conditions necessary for providers to make more accurate and timely readiness determinations. This may ultimately reduce safety and mission failure risks enterprise-wide, and policymakers should consider formalizing and integrating the B-REDI model into current Army BH practice.

Measurement of End-User Computing Satisfaction

2008 ◽  
pp. 894-896
Author(s):  
Rodney A. Reynolds
Keyword(s):  
Decision Making ◽  
Decision Analysis ◽  
User Satisfaction ◽  
Ease Of Use ◽  
Computer Applications ◽  
End User ◽  
Satisfaction Scale ◽  
End User Computing

Doll and Tofkzadeh (1988) developed their measure of End-User Computing Satisfaction because ‘decision analysis’ (examination of specific uses of computer applications in decision making) is “generally not feasible” (p. 259) but that satisfaction is a reasonable surrogate for assessing use. Doll and Tofkzadeh claim evidence from other studies support an expectation that satisfaction leads to use (as opposed to use leading to satisfaction. The Doll and Tofkzadeh study focused more on broad notions of systems and applications (Mini or mainframes, Micro-computer applications, Analysis, and Monitor applications). The End-User Computing Satisfaction scale is multidimensional instrument. Doll and Tofkzadeh (1988) started with 40 items and reduced those first to 18 items and then reduced the scale further to a final set of 12 items. The dimensions of the End-User Satisfaction scale are Content, Accuracy, Format, Ease of use, and Timeliness.

scholarly journals Methodology of the U.S. Army’s Suicide Prevention Leadership Tool Study: The Behavioral Health Readiness and Suicide Risk Reduction Review (R4)

2020 ◽  
Author(s):  
Justin M Curley ◽  
Farifteh F Duffy ◽  
Paul Y Kim ◽  
Kristina M Clarke-Walper ◽  
Katie L Nugent ◽  
...  
Keyword(s):  
Decision Making ◽  
Behavioral Health ◽  
Risk Reduction ◽  
Suicide Prevention ◽  
Evaluation Study ◽  
Repeated Measurements ◽  
Control Group ◽  
Walter Reed Army Institute ◽  
Quasi Experimental ◽  
The U.S

Abstract Introduction The Secretary of the U.S. Army issued two directives in late 2017 to directly combat the problem of suicide in the U.S. Army. The first was to develop an Army tool to assist commanders and first-line leaders in preventing suicide and improving behavioral health (BH) outcomes, which has been previously published as the BH Readiness and Risk Reduction Review (R4). The second was to conduct an evaluation study of the tool with Army units in the field. This study is the first to empirically examine the Army’s tool-based methods for identifying and caring for the health and welfare of soldiers at risk for suicide, and this article outlines the methodology employed to study the effectiveness of the R4 tools and accomplish the Secretary’s second directive. Methods The Walter Reed Army Institute of Research Institutional Review Board approved the R4 study. The study employed a repeated measurements in pre/post quasi-experimental design, including a nonequivalent but comparable business-as-usual control group. The R4 intervention consisted of the R4 tools, accompanying instructions, and an orientation. Samples were drawn from two geographically separated U.S. Army divisions in the continental United States, each composed of four comparable brigades. Study implementation consisted of three phases and three data collections over the course of 12 months. Soldiers completed anonymous survey instruments to assess a range of health factors, behaviors, characteristics, tool-related decision-making processes, and the frequency, type, and quality of interactions between soldiers and leaders. Results The R4 study commenced on May 6, 2019, and concluded on June 4, 2020. Sample size goals were achieved for both the divisions at all three data collection time points. Conclusions The methodology of the R4 study is critical for the U.S. Army from both a precedential and an outcome-based standpoint. Despite the use of many previous tools and programs for suicide prevention, this is the first time the Army has been able to empirically test the effectiveness of tool-supported decision-making among Army units in a rigorous fashion. The methodology of such a test is a critical marker for future interventional inquiries on the subject of suicide in the Army, and the results will allow for more informed decision-making by leaders when approaching these ongoing challenges.

Measurment of End-User Computing Satisfaction

2007 ◽  
pp. 293-295
Author(s):  
R. Reynolds
Keyword(s):  
Decision Making ◽  
Decision Analysis ◽  
User Satisfaction ◽  
Ease Of Use ◽  
Computer Applications ◽  
End User ◽  
Satisfaction Scale ◽  
End User Computing

Doll and Tofkzadeh (1988) developed their measure of End-User Computing Satisfaction because ‘decision analysis’ (examination of specific uses of computer applications in decision making) is “generally not feasible” (p. 259) but that satisfaction is a reasonable surrogate for assessing use. Doll and Tofkzadeh claim evidence from other studies support an expectation that satisfaction leads to use (as opposed to use leading to satisfaction. The Doll and Tofkzadeh study focused more on broad notions of systems and applications (Mini or mainframes, Micro-computer applications, Analysis, and Monitor applications). The End-User Computing Satisfaction scale is multidimensional instrument. Doll and Tofkzadeh (1988) started with 40 items and reduced those first to 18 items and then reduced the scale further to a final set of 12 items. The dimensions of the End-User Satisfaction scale are Content, Accuracy, Format, Ease of use, and Timeliness.

Assessing Team Detailing End-user Satisfaction

2004 ◽  
Author(s):  
Kimberly P. Whittam ◽  
Zannette A. Uriell ◽  
Rorie N. Harris
Keyword(s):  
User Satisfaction ◽  
End User

Structuring and visualization of indicators in multidimensional data cubes

2018 ◽  
pp. 79-87
Author(s):  
E. E. Akimkina
Keyword(s):  
Decision Making ◽  
Data Processing ◽  
Data Management ◽  
Multidimensional Data ◽  
End User ◽  
Processing Technologies ◽  
Data Cubes ◽  
Multidimensional Visualization ◽  
Multidimensional Data Cubes ◽  
Practical Recommendations

The problems of structuring of indicators in multidimensional data cubes with their subsequent processing with the help of end-user tools providing multidimensional visualization and data management are analyzed; the possibilities of multidimensional data processing technologies for managing and supporting decision making at a design and technological enterprise are shown; practical recommendations on the use of domestic computer environments for the structuring and visualization of multidimensional data cubes are given.

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