Impact of Mobile Health (mHealth) Use by Community Health Workers on the Utilization of Maternity Care in Rural Malawi: A Time Series Analysis [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
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".