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Related Concept Videos

Data Validation01:03

Data Validation

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
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Related Experiment Video

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Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
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Digital Intervention Barriers Scale-7 (DIBS-7): Development, Evaluation, and Preliminary Validation.

Giovanni Ramos1, Amanda Kay Montoya1, Hayley Renee Hammons1

  • 1Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States.

JMIR Formative Research
|April 6, 2023
PubMed
Summary
This summary is machine-generated.

The Digital Intervention Barriers Scale-7 (DIBS-7) is a new, validated tool to measure barriers in digital mental health interventions (DMHIs). This scale helps identify challenges impacting patient engagement and treatment success in online mental health services.

Keywords:
barriersdevelopmentdigital mental health interventionmeasurepsychometricsscalevalidation

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Area of Science:

  • Digital Health
  • Mental Health Interventions
  • Psychometrics

Background:

  • Digital mental health interventions (DMHIs) offer solutions to access barriers in mental healthcare.
  • However, DMHIs face unique challenges impacting user enrollment, adherence, and retention.
  • Currently, a lack of standardized, validated measures exists for these digital barriers.

Purpose of the Study:

  • To introduce the preliminary development and evaluation of the Digital Intervention Barriers Scale-7 (DIBS-7).
  • To provide a standardized tool for assessing barriers within digital mental health programs.

Main Methods:

  • Iterative mixed-methods approach (QUAN → QUAL) for item generation based on participant feedback (n=259) from a DMHI trial.
  • Expert review for item refinement, followed by psychometric evaluation (EFA, CFA) on a larger sample (n=559).
  • Criterion-related validity assessed via correlations with treatment engagement constructs.

Main Results:

  • A 7-item unidimensional scale (DIBS-7) demonstrated high internal consistency (α=.82, ω=0.89).
  • Preliminary criterion-related validity was supported by significant correlations with treatment expectations, module completion, check-ins, and satisfaction.
  • DIBS-7 scores negatively correlated with engagement and satisfaction, indicating its utility.

Conclusions:

  • The DIBS-7 shows preliminary evidence as a valuable, brief scale for measuring barriers in DMHIs.
  • This tool can aid clinicians and researchers in understanding factors affecting treatment adherence and outcomes.
  • The DIBS-7 facilitates the optimization of digital mental health services.