The Impact of Mobile Health (mHealth) in Maternal Health Services [Response to Letter]
Bibliographic record
Abstract
Chiyembekezo Kachimanga,1,2 Wingston Felix Ng’ambi,3 Doctor Kazinga,1 Enoch Ndarama,4 Mercy Ambogo Amulele,5 Fabien Munyaneza,1 Ibukun-Oluwa O Abejirinde,6,7 Thomas van den Akker,2,8 Alexandra V Kulinkina1,9,10 1Partners in Health Malawi, Neno, Malawi; 2Athena Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands; 3Health Economics and Policy Unit, Department of Health Systems and Policy, Kamuzu University of Health Sciences, Lilongwe, Malawi; 4Ministry of Health, Neno, Malawi; 5Medic, Nairobi, Kenya; 6Women College Hospital Institute for Health System Solutions and Virtual Care, Toronto, ON, Canada; 7Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; 8Department of Obstetrics and Gynaecology, Leiden University Medical Center, Leiden, Netherlands; 9Swiss Tropical and Public Health Institute, Allschwil, Switzerland; 10University of Basel, Basel, SwitzerlandCorrespondence: Chiyembekezo Kachimanga, Partners in Health Malawi, Post Office Box 56, Neno, Malawi, Email chembekachimanga@yahoo.co.uk
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.021 | 0.021 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".