Beyond the incubator: applying a “one health” approach in the NICU
Bibliographic record
Abstract
A "one health" approach recognises that human health, animal health and planetary health are closely interlinked and that a transdisciplinary approach is required to fully understand and maintain global health. While, by necessity, Neonatal Intensive Care has traditionally focused on the acutely unwell newborn, the avoidance of long-term harm is core to many management decisions. The COVID 19 pandemic and climate crisis have brought into sharp relief the importance of a "one health" approach as part of long-term health promotion in the holistic care of neonates, who may survive to experience the burden of future environmental crises. This narrative review seeks to integrate what we know about "one health" issues in the neonatal intensive care unit, notably antimicrobial resistance and climate change, and suggest "everyday changes" which can be utilised by practitioners to minimise the impact of neonatal intensive care on these global health issues. Many of the changes suggested not only represent important improvements for planetary health but are also core to good neonatal practice. IMPACT: Neonatal patients are likely to bear the burden of future environmental crises including pandemics and climate related disasters. While the focus of intensive care practitioners is acute illness, awareness of "one health" problems are important for our smallest patients as part of preventing long-term harm. High quality neonatal care can benefit both the planet and our patients.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".