Saturation sampling in qualitative health research is a methodological concept used to determine when to stop data collection, based on the researcher's judgment that no new information is being added. This concept is frequently employed in qualitative studies across various fields, particularly in health research. The paper discusses the theoretical underpinnings of saturation sampling, including the challenges in its acceptance and operationalization, the appropriate size of intentional samples, the significance of valuing repeated information or differences in sample reports, and the misuse of terms related to saturation. It also explores possible metaphors to understand the concept. The paper emphasizes the importance of transparency and clarity in research reports, particularly in the data collection phase, as these are key indicators of scientific rigor. Saturation sampling is defined as the point at which data collection stops because the information gathered no longer adds new insights. This concept was originally introduced by Glaser and Strauss, who described it as the empirical confirmation that a category is saturated, based on criteria such as data limits, integration with theory, and theoretical sensitivity. The paper also discusses the role of sampling in validating research, highlighting the importance of the sample's representativeness and the researcher's theoretical framework. It addresses the challenges of using non-probability sampling in qualitative research, emphasizing the need for congruence between theoretical paradigms and research methods. The paper critiques the misuse of the term "saturation" and discusses the importance of clearly defining the criteria for stopping data collection. The paper also explores the implications of saturation sampling in different contexts, such as the need for a representative sample in health research and the challenges of interpreting data in qualitative studies. It highlights the importance of considering the researcher's theoretical perspective and the cultural and social context of the study. The paper concludes that saturation sampling is a complex concept influenced by cognitive and affective factors, and that transparency in reporting the factors contributing to the decision to stop data collection is essential for scientific rigor.Saturation sampling in qualitative health research is a methodological concept used to determine when to stop data collection, based on the researcher's judgment that no new information is being added. This concept is frequently employed in qualitative studies across various fields, particularly in health research. The paper discusses the theoretical underpinnings of saturation sampling, including the challenges in its acceptance and operationalization, the appropriate size of intentional samples, the significance of valuing repeated information or differences in sample reports, and the misuse of terms related to saturation. It also explores possible metaphors to understand the concept. The paper emphasizes the importance of transparency and clarity in research reports, particularly in the data collection phase, as these are key indicators of scientific rigor. Saturation sampling is defined as the point at which data collection stops because the information gathered no longer adds new insights. This concept was originally introduced by Glaser and Strauss, who described it as the empirical confirmation that a category is saturated, based on criteria such as data limits, integration with theory, and theoretical sensitivity. The paper also discusses the role of sampling in validating research, highlighting the importance of the sample's representativeness and the researcher's theoretical framework. It addresses the challenges of using non-probability sampling in qualitative research, emphasizing the need for congruence between theoretical paradigms and research methods. The paper critiques the misuse of the term "saturation" and discusses the importance of clearly defining the criteria for stopping data collection. The paper also explores the implications of saturation sampling in different contexts, such as the need for a representative sample in health research and the challenges of interpreting data in qualitative studies. It highlights the importance of considering the researcher's theoretical perspective and the cultural and social context of the study. The paper concludes that saturation sampling is a complex concept influenced by cognitive and affective factors, and that transparency in reporting the factors contributing to the decision to stop data collection is essential for scientific rigor.