Your browser doesn't support javascript.
loading
Analysis of FRAME data (A-FRAME): An analytic approach to assess the impact of adaptations on health services interventions and evaluations.
Mui, Heather Z; Brown-Johnson, Cati G; Saliba-Gustafsson, Erika A; Lessios, Anna Sophia; Verano, Mae; Siden, Rachel; Holdsworth, Laura M.
Afiliação
  • Mui HZ; Division of Primary Care and Population Health, Department of Medicine School of Medicine, Stanford University Palo Alto California USA.
  • Brown-Johnson CG; Division of Primary Care and Population Health, Department of Medicine School of Medicine, Stanford University Palo Alto California USA.
  • Saliba-Gustafsson EA; Division of Primary Care and Population Health, Department of Medicine School of Medicine, Stanford University Palo Alto California USA.
  • Lessios AS; Division of Primary Care and Population Health, Department of Medicine School of Medicine, Stanford University Palo Alto California USA.
  • Verano M; Division of Primary Care and Population Health, Department of Medicine School of Medicine, Stanford University Palo Alto California USA.
  • Siden R; Division of Primary Care and Population Health, Department of Medicine School of Medicine, Stanford University Palo Alto California USA.
  • Holdsworth LM; Division of Primary Care and Population Health, Department of Medicine School of Medicine, Stanford University Palo Alto California USA.
Learn Health Syst ; 8(1): e10364, 2024 Jan.
Article em En | MEDLINE | ID: mdl-38249838
ABSTRACT

Introduction:

Tracking adaptations during implementation can help assess and interpret outcomes. The framework for reporting adaptations and modifications-expanded (FRAME) provides a structured approach to characterize adaptations. We applied the FRAME across multiple health services projects, and developed an analytic approach to assess the impact of adaptations.

Methods:

Mixed methods analysis of research diaries from seven quality improvement (QI) and research projects during the early stages of the COVID-19 pandemic. Using the FRAME as a codebook, discrete adaptations were described and categorized. We then conducted a three-step analysis plan (1) calculated the frequency of adaptations by FRAME categories across projects; (2) qualitatively assessed the impact of adaptations on project goals; and (3) qualitatively assessed relationships between adaptations within projects to thematically consolidate adaptations to generate more explanatory value on how adaptations influenced intervention progress and outcomes.

Results:

Between March and July 2020, 42 adaptations were identified across seven health services projects. The majority of adaptations related to training or evaluation (52.4%) with the goal of maintaining the feasibility (66.7%) of executing projects during the pandemic. Five FRAME constructs offered the most explanatory benefit to assess the impact of adaptations on program and evaluation goals, providing the basis for creating an analytic approach dubbed the "A-FRAME," analysis of FRAME data. Using the A-FRAME, the 42 adaptations were consolidated into 17 succinct adaptations. Two QI projects discontinued altogether. Intervention adaptations related to staffing, training, or delivery, while evaluation adaptations included design, recruitment, and data collection adjustments.

Conclusions:

By sifting qualitative data about adaptations into the A-FRAME, implementers and researchers can succinctly describe how adaptations affect interventions and their evaluations. The simple and concise presentation of information using the A-FRAME matrix can help implementers and evaluators account for the influence of adaptations on program outcomes.
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Qualitative_research Aspecto: Implementation_research Idioma: En Revista: Learn Health Syst Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Qualitative_research Aspecto: Implementation_research Idioma: En Revista: Learn Health Syst Ano de publicação: 2024 Tipo de documento: Article