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A Practical Guide to Applying the Delphi Technique in Mental Health Treatment Adaptation: The Example of Enhanced Problem-Solving Training (E-PST).

Paul R KingGregory P BeehlerKerry DonnellyJennifer S FunderburkLaura O Wray
Published in: Professional psychology, research and practice (2021)
Expert consensus methods, such as the Delphi procedure, are commonly employed in consumer, education, and health services research. However, the utility of this methodology has not widely been described in relation to mental health treatment adaptation efforts. This gap is noteworthy given that evidence-based treatments are often modified in terms of core intervention content, method of delivery, and target populations. Expert consensus methods such as the Delphi procedure offer multiple practical benefits (e.g., flexibility, resource-efficiency) for psychologists who need to adapt existing treatments to meet new research and clinical practice needs. The purpose of this paper is to provide a brief overview of the Delphi procedure, and to offer a practical guide to using this method for treatment adaptation. An example is offered using our team's application of a three-round Delphi procedure to render content and context modifications to an existing problem-solving intervention to optimize its use with a new treatment population. Data were collected from Department of Veterans Affairs clinical subject matter experts. Round 1 utilized semi-structured interviews to determine necessary protocol features and modifications. Rounds 2-3 utilized a forced-choice survey and feedback loop to evaluate expert consensus. More than 91% of rated items reached consensus following Round 2, with the remainder following Round 3. Recommended modifications included minor structural and content edits, and re-balancing time allotments. We conclude that consensus methods may facilitate treatment adaptation efforts, enhance treatment feasibility, and promote content and ecological validity. Considerations for future Delphi-based treatment adaptations are offered.
Keyphrases
  • mental health
  • randomized controlled trial
  • healthcare
  • risk assessment
  • climate change
  • combination therapy
  • big data
  • cross sectional
  • artificial intelligence
  • deep learning
  • health information