Bibliographic record
Abstract
There have been significant improvements in maternal and child mortality in recent years; however, an unacceptable number of mothers and babies still die or suffer preventable morbidities, especially in low- and middle-income countries (LMICs).1 This is due to challenges of both supply and demand in healthcare service provision, most of which can be addressed by effective mobile health (mHealth) technologies, as illustrated in the review by Dol et al.2 published in this issue of the JBI Database of Systematic Reviews and Implementation Reports. The perinatal period is a critical moment for the use of smart technologies that facilitate access to much-needed health promotion, health technologies and services, including antenatal care, delivery, vaccination and breastfeeding services. The World Health Organization (WHO) Global Observatory for eHealth defines mHealth as medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs) and other wireless devices.3 This could involve one or more of a mobile phone's core functions of voice and short message service (SMS). It could also use more complex functionalities and applications like general packet radio service, third- and fourth-generation (3G and 4G) mobile telecommunication systems, global positioning system (GPS) and Bluetooth technology. The mHealth practice landscape is both widening and gaining interest, demonstrated by the numerous mHealth tools that are appearing in LMICs, often times with leadership from LMICs.4 There is increasing ownership of mHealth tools like mobile phones, Internet and other information and communication technology tools. There is considerable, although not enough, investment by funders, development agencies and governments. The Bill and Melinda Gates Foundation and Grand Challenges Canada have recently taken a bold step in promoting and funding innovative ideas that promote health outcomes, and these have supported several mHealth projects in LMICs.5 However, this boldness has yet to gather momentum in governments in LMICs, and the big question of “who pays?” still remains.6 There are some reported mHealth projects that have shown promising results in LMICs relevant to perinatal care, such as the Millennium Village Project funded by Sony Ericsson in Ghana, Project Optimize funded by WHO and Program for Appropriate Technology in Health (PATH) in Albania, and the BornFyne project funded by Grand Challenges Canada in Cameroon. Scaling out to pilot still remains a challenge, however, due to a lack of confidence from stakeholders. The future of health care is digital. LMICs have embraced the digital revolution much better than the industrial revolution. Mankind has, however, received digital health with some hesitation. In order to roll out digital health programs, governments, development agencies and users should take bold steps to embrace this technology. mHealth can expand treatment options for healthcare workers and their clients. However, mHealth interventions must be critically assessed through a systematic scientific approach to ensure that what works in mHealth is clearly documented and available for practitioners, policy makers and patients.
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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 | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".