Digitalization of home-based records for maternal, newborn, and child health: a scoping review
Bibliographic record
Abstract
Abstract Background At least 163 countries use a form of home-based record, a document to record health information kept at home. These are predominantly paper-based, although some countries are digitalizing home-based records for improved access and use. This scoping review aimed to identify efforts already undertaken for the digitalization of home-based records for maternal, newborn, and child health (MNCH) and lessons learned moving forward, by mapping the available peer-reviewed and grey literature. Methods The scoping review was guided by Arskey and O’Malley’s framework. A literature search of references published from 2000 until 2021 was conducted in Medline, Embase, CINAHL, EBM reviews, Google Scholar, IEEE Xplore as well as a grey literature search. Title and abstract and full texts were screened in Covidence. A final data extraction sheet was generated in Excel. Results The scoping review includes 107 references that cover 120 unique digital interventions. Most of the included references are peer-reviewed articles in English language published after 2015. Of the 120 unique digital interventions, 80 (66.7%) are used in 31 different countries and 40 (33.3%) are globally available pregnancy applications. Out of the 80 digitalization efforts from countries, most are concentrated in high-income countries ( n =68, 85%). Maternal health ( n =73; 61%) and child health ( n =60; 50%) are the main health domains covered; the main users are pregnant women ( n =57; 48%) and parents/caregivers ( n =43; 36%). Conclusions Most digital home-based records for MNCH are centered in high-income countries and revolve around pregnancy applications or portals for home access to health records covering MNCH. Lessons learned indicate that the success of digital home-based records correlates with the usability of the intervention, digital literacy, language skills, ownership of required digital devices, and reliable electricity and internet access. The digitalization of home-based records needs to be considered together with digitizing patient health records.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".