Global health research as seen through a health-system lens.
Bibliographic record
Abstract
We recently participated in a research internship at Great Lakes University of Kisumu, Kenya, that brought together colleagues from five lowand middle-income countries. The question of how to meet the Millennium Development goal for maternal health was a recurring point of discussion. Interns observed that while some progress has been made, high maternal mortality ratios, late and infrequent antenatal visits, and a substantial proportion of deliveries being assisted by unskilled birth attendants in many parts of Sub-Saharan Africa indicate that much remains to be done (Crowe, Utley, Costello, & Pagel, 2012). We reflected on the state of the evidence, successes and gaps, and efforts being made to address what at times seems an intransigent problem. We asked ourselves: How can nurses and midwives use research to make a difference, and would a health-system lens provide a different orientation to our research approach? We begin with three brief scenarios, each highlighting a critical dimension of the gap in maternal care. We then consider the value of using a health-system lens to guide research in this field.
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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 | 0.047 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.008 | 0.066 |
| Scholarly communication | 0.027 | 0.031 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.011 | 0.014 |
| 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".