COVID-19 preparedness—a survey among neonatal care providers in low- and middle-income countries
Bibliographic record
Abstract
OBJECTIVE: To evaluate COVID-19 pandemic preparedness, available resources, and guidelines for neonatal care delivery among neonatal health care providers in low- and middle-income countries (LMICs) across all continents. STUDY DESIGN: Cross-sectional, web-based survey administered between May and June, 2020. RESULTS: Of 189 invited participants in 69 LMICs, we received 145 (77%) responses from 58 (84%) countries. The pandemic provides significant challenges to neonatal care, particularly in low-income countries. Respondents noted exacerbations of preexisting shortages in staffing, equipment, and isolation capabilities. In Sub-Saharan Africa, 9/35 (26%) respondents noted increased mortality in non-COVID-19-infected infants. Clinical practices on cord clamping, isolation, and breastfeeding varied widely, often not in line with World Health Organization guidelines. Most respondents noted family access restrictions, and limited shared decision-making. CONCLUSIONS: Many LMICs face an exacerbation of preexisting resource challenges for neonatal care during the pandemic. Variable approaches to care delivery and deviations from guidelines provide opportunities for international collaborative improvement.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".