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 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 | 0.023 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.023 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".