Bibliographic record
Abstract
BACKGROUND: There are pervasive and documented disparities in maternal and infant outcomes related to race and ethnicity. Critical awareness is growing in our current cultural environment about strategies to improve health equity, the need to challenge implicit bias, and dismantle racism in healthcare to decrease racial health inequities. METHODS: In this article, we provide a summary of health inequities that exist within the perinatal/neonatal population and offer strategies for initiating conversations and improving health equity by challenging bias and increasing diversity. RESULTS: Transformative leaders must understand the evidence related to health disparities, understand social drivers of inequity issues, and identify solutions to influence change. IMPLICATIONS FOR PRACTICE: With heightened awareness and examination of implicit bias, we can improve care for all infants and their families. IMPLICATIONS FOR RESEARCH: We need to continue research and quality improvement efforts to improve health equity. Furthermore, research is needed that focus on social determinants of health as drivers of preterm delivery and birth complications, rather than biological (eg, racialized) factors.Video Abstract available at:https://journals.lww.com/advancesinneonatalcare/Pages/videogallery.aspx?autoPlay=false&videoId=42.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.003 |
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".