Critical Analysis of the Current Status of Transdisciplinary Research in Neonatology and Sustainable Development Goals
Bibliographic record
Abstract
Background: Neonatology, a discipline that deals with a variety of medical conditions such as prematurity, breathing, infections and birth defects, has seen rapid development and significant advancements during the past decades to ensure the survival and healthy life of newborns. Although the efforts of medical scientists following the multidisciplinary and interdisciplinary approaches have successfully reduced the neonatal mortality rate from about 5 million before 2000s to 2.3 million as of 2022, but stills challenges in understanding and addressing the complex neonatal health issues remain unsolved, which invites the necessity to apply complex and comprehensive transdisciplinary research approaches. Transdisciplinary research approaches are complex types of approaches applied for complex types of issues, engaging all stakeholders of society, so a holistic approach to understand the neonatal health, and the impacts of environmental, social, and biological factors should be studied. This article has shed light on the significance of transdisciplinarity which has proven its role in the development of a sustainable society and these holistic research approaches, can ensure meaningful results to reduce the mortality rate of newborns from 50% to 5%.
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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 | 0.084 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".