At the end
Mahita v -
Hello, and welcome back to my last senior project blog post! Thank you to everyone who has read these posts, and I hope you’ve learned something along with me during this project.
For my final week, I first made a couple of adjustments to my logistic regression model by changing the variables for the poverty and income covariates. However, this didn’t really make much of a difference in my results, so I just adjusted the values that did change in my data tables.
Since I finished writing my research paper, I wanted to point out the limitations and the future directions of my project. One limitation of this project is that the data were self-reported, which makes it subject to recall bias or social desirability bias. Recall bias occurs when participants do not accurately remember past events or experiences, leading to misreporting. Social desirability bias refers to the tendency of respondents to answer questions in a manner that they believe will be viewed favorably by others, rather than providing truthful responses. To address missing data, imputation techniques were applied, which, although useful for preserving sample size, introduce their own limitations. Imputed values are estimates and may not reflect true responses, potentially introducing bias, especially if data are not missing at random. Certain imputation methods can also reduce variability in the dataset, affecting the accuracy of statistical inference. Moreover, the presence of missing data limited the inclusion of additional covariates that could have provided deeper insights.
Future studies should investigate other themes in healthcare systems, such as timeliness, emotions, and information. This study has demonstrated that there is a varying degree of impact based on which factor is analyzed. Since the results of the psychosocial support theme indicate a need for increased cultural competence training, it would also be valuable to investigate current training in medical school and assess what further themes need to be incorporated to ensure future healthcare providers are equipped to deliver empathetic, inclusive, and patient-centered care. This includes evaluating how well existing curricula address topics such as implicit bias, structural racism, communication across cultural differences, and the psychosocial needs of marginalized communities. To gain a broad understanding of current psychosocial support practices and gaps in culturally competent care, a scoping analysis of semi-structured interviews conducted with a randomized sample of hospitals across the United States could be undertaken. This would allow for the identification of recurring themes, underrepresented areas, and institutional differences, providing a foundation for future policy or curriculum improvements.
Working on this project has taught me so much about data analysis and how I can blend the different interests I have in my research, but it has also bolstered my problem-solving skills and resilience. I hope to conduct similar research in the future and potentially get this project published. Once again, thank you to everyone who stuck around for all 10 posts!