Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».