Companion Files for Racial Differences in Patient Experience and Diabetes Management Outcomes among Reproductive-Age Women
Bibliographic record
Abstract
The STATA do files for creating recodes and analyzing the data files will be provided in this folder for each paper once submitted for publication. Revised do files will be uploaded after the peer review process is complete if there are any changes to the files. <br><br>The data for this study are publicly available on the Medical Expenditure Panel Survey website (https://meps.ahrq.gov/mepsweb/). <br><br><b>Summary</b><br>Significant racial and ethnic disparities in cardiometabolic diseases, such as diabetes, underline entrenched health inequalities in the United States. Non-pregnant, non-Hispanic black women of reproductive age (18-45 years) are more likely to have diagnosed and undiagnosed diabetes, which increases their risk of maternal morbidity and mortality during the perinatal period. Adherence to disease management and monitoring during the preconception period is crucial, especially among non-Hispanic black women who are disproportionately impacted by maternal morbidity and mortality. Studies have shown positive patient experiences are associated with adherence to recommended medication and treatment, preventive care use, and self-rated health outcomes. There is a dearth of studies, however, examining the effects of patient experiences (and racial differences in patient experiences) on chronic disease management outcomes specifically among non-pregnant, reproductive-age women with diabetes. An understanding of these associations have important implications for maternal morbidity and mortality. The goal of this study is to use the Medical Expenditure Panel Survey datasets (2012-2017 longitudinal files) and robust statistical modeling techniques to investigate racial differences in patient experience among non-pregnant, reproductive-age women with diabetes and its relation to ratings of health care received, diabetes care self-efficacy, and diabetes care monitoring. This study provides important information for researchers, clinicians, and policy-makers. The research addresses the Maternal and Child Health Bureau (MCHB) Strategic Research Issue II: MCH services and systems of care efforts to eliminate health disparities and barriers to health care access for MCH populations. This study informs the development of equitable clinical patient-centered practices that promote optimal disease management among diverse women and reduce racial and ethnic disparities in maternal health outcomes. It also strengthens and expands MCH Services Block Grant National Performance and Population Priority Domain I: “Well-Woman Visits and Preconception/Interconception Health”. This study is expected to help determine whether positive patient experiences can improve women’s confidence in their abilities to manage their diabetes, and increase their likelihood of receiving recommended diabetes care during the preconception or interconception period. By elucidating the mechanisms by which promoting patient-centered diabetes care interventions during the preconception/interconception period might improve disease management, our study can inform practices and policies that contribute to the attainment of following Healthy People 2020 Maternal, Infant, and Child Health (MIC) Objectives: Increase the proportion of women delivering a live birth who received preconception care services and practiced key recommended preconception health behaviors (MICH-16); Reduce the rate of maternal illness and complications due to pregnancy (MICH-6); and Reduce the rate of maternal mortality (MICH-5). Furthermore, our study findings are expected identify the patient experiences that have the greatest impact on diabetes management outcomes, and can lead to policy changes for provider reimbursements for demonstrating quality patient-provider interactions. By providing insights into ways health care professionals can better communicate with this at-risk population, our study is relevant to the attainment of Healthy People 2020 Health Communication and Health Information Technology (HC/HIT) Objectives: Increase the proportion of persons who report that their health care providers have satisfactory communication skills (HC/HIT-2) and Increase the proportion of persons who report that their health care providers always involved them in decisions about their health care as much as they wanted (HC/HIT-3).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".