Increasing Knowledge and Health Literacy about Preterm Births in Underserved Communities: An Approach to Decrease Health Disparities, a Pilot Study
Bibliographic record
Abstract
INTRODUCTION: Health disparities can negatively impact subsets of the population who have systematically experienced greater socioeconomic obstacles to health. For example, health disparities between ethnic and racial groups continue to grow due to the widening gap in large declines in infant and fetal mortality among Caucasians compared to Black non-Hispanic or African Americans. According to the American Congress of Obstetricians and Gynecologists, preterm birth remains a leading cause of infant morbidity and mortality. The purpose of our study is to determine if the computer-based educational modules related to preterm birth health literacy and health disparity with a pre-test and post-test can effectively increase health knowledge of our participants in targeted underserved communities within the Richmond-metro area. METHODS: This was a pilot study in the Richmond-Metro area. Participants were required to be over the age of 18, and had to electronically give consent. Descriptive statistics, means and standard deviations, and Paired t-tests were conducted in SPSS 22.0. RESULTS: There were 140 participants in the pilot study. P<.05 was set as significant and all four modules had a P<.000. The males were not significant with modules: Let's Talk Patient & Provider Communication P<.132 and It Takes a Village P<.066. Preterm birth status yes all of the findings were statistically significant P<.000. Preterm birth status no Let's Talk Patients & Provider Communication was not significant P<.106. CONCLUSION: Overall, researchers found that with a strong research methodology and strong content relevant to the community, the participants demonstrated an increase in their knowledge in health literacy and preterm birth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".