COVID-19, maternal and child health, and nutrition repository: A portal to provide updated information to health professionals amid a pandemic and continuously evolving environment
Bibliographic record
Abstract
In the early months of the COVID-19 pandemic, there was a lack of consistent guidance despite pressing questions from health professionals regarding how to limit the spread of SARS-CoV-2 while also providing optimal maternal and child health care. In response, the “COVID-19, Maternal and Child Health, and Nutrition” literature repository was assembled, mobilizing a team of graduate students to provide concise summaries of emerging peer-reviewed publications. What began as a small trickle of evidence from China quickly grew into an overwhelming amount of information – roughly 120-150 publications per week pertaining to maternal and child health in the context of COVID-19. The authors present their experiences constructing, staffing, maintaining, and disseminating this literature repository while also providing opportunities for growth and learning for the graduate student volunteers who made it possible. Many of these students also served on the frontlines of the pandemic as healthcare providers, often sharing how helpful it was for their work with patients to stay up to date with emerging research. This case study is intended to serve as a blueprint for current and future repositories, particularly those that aim to incorporate service learning into graduate education.
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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.108 | 0.048 |
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".