Out now: Want to provide a more well rounded discussion of the #transferability of your #qualitativeresearch? @Megan_EL_Brown @bobrien_15 and I explore transferability as resonance, applicability and theoretical engagement @ClinicalTeacher tinyurl.com/6y2fuzmh #openaccess
@ReneeStalmeijer @Megan_EL_Brown @bobrien_15 @ClinicalTeacher If you read only one paper this week, make it THIS ONE!!!!!! @sherbino @drjfrank @mededdoc @TChanMD @r_ajjawi
Love it -- awesome paper! One clarifying point about the analogous concept of generalizability, which we talk about in quant. It's about more than just the representativeness of the sample. Generalizability (aka, external validity) is the degree to which study findings can be applied beyond the specific conditions of the original study to other persons, settings, treatments, or outcomes. And it is influenced by several key factors, including: Sample Characteristics: The degree to which the sample represents the population of interest affects generalizability. A more representative sample (in terms of demographics, behaviors, conditions, etc.) allows for broader generalizations. Sampling Method: The method by which participants are selected plays a crucial role. Random sampling methods enhance the likelihood that the sample accurately reflects the broader population, improving generalizability. Study Settings: The settings in which the research is conducted can influence generalizability. Studies conducted in highly controlled environments (like laboratories) may have limited applicability to more naturalistic settings. Study Design: The design of the study, including the methods of data collection and analysis, can impact generalizability. Designs that closely mimic real-world conditions are more likely to produce generalizable findings. Intervention Specificity: The degree to which the treatment or intervention is specific to particular conditions, populations, or settings can affect generalizability. More broadly applicable interventions are more likely to have generalizable outcomes. Outcome Measures: The choice of outcome measures influences generalizability. Outcomes that are widely relevant and applicable across different contexts and populations support broader generalizations. Time Frame: The period over which the study is conducted can impact its generalizability. Longitudinal studies that account for changes over time may offer more generalizable insights than cross-sectional studies. Cultural and Contextual Factors: Cultural, economic, and social contexts of the study population affect generalizability. Studies that take these factors into account can often be more easily generalized to other contexts. Statistical Power: Studies with sufficient statistical power (adequate sample size to detect an effect if one exists) are more likely to produce results that are generalizable. Replication: The extent to which study findings are replicated across different studies, settings, and populations also informs generalizability. Consistent findings across multiple studies strengthen the case for generalization. #MedEd