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Evidences have demonstrated the effectiveness of early essential newborn care. However, the implementation of early essential newborn care is suboptimal. The aim is to identify and synthesise the barriers and facilitators impacting the implementation of early essential newborn care in low- and middle-income countries.
PubMed, EMBASE, Cochrane Central Register of Controlled Trials, PsycINFO, CINAHL, CNKI, Wan Fang Data, SinoMed and Google Scholar.
Two authors independently screened, performed quality assessment using the Mixed Methods Appraisal Tool and extracted data. This review includes papers that reported the barriers and facilitators of implementing early essential newborn care in low- and middle-income countries from the view of healthcare providers. Barriers and facilitators were coded according to the consolidated framework for implementation research in a deductive way and then been inducted into five common themes. This review followed synthesis without meta-analysis reporting guideline.
A total of 28 studies were included. Five inductive common themes influencing implementing early essential newborn care in low- and middle-income countries were system-level healthcare factors, healthcare providers’ knowledge and beliefs, the requirements of mothers or families, adapting to routine practice and the working climate of organisation.
The factors were from system level, facility level and individual level and were inducted into five themes. Based on this review, decision-makers could tailor implementing strategies to narrow the gap between the evidence and implementation.
The study offers guidance for health professionals to identify barriers and facilitators in implementing early essential newborn care and make tailored strategies when implementing early essential newborn care.
No patient or public contributions.
by Yan Liu, Yan Qu
The continuous development of science and technology has led to the gradual digitization and intelligence of campus construction. To apply facial recognition technology to construct smart libraries in higher education, this study optimizes traditional facial recognition algorithm models. Firstly, a smart management system for university libraries is designed with facial recognition as the core, and secondly, the traditional FaceNet network is optimized. Combined with MobileNet, Attention mechanism, Receptive field module and Mish activation function, the improved multitask face recognition convolutional neural network is built and used in the construction of university smart library. The performance verification of the constructed model shows that the feature matching error value of the model in a stable state is only 0.04. The recognition accuracy in the dataset is as high as 99.05%, with a recognition error as low as 0.51%. The facial recognition model used in university smart libraries can achieve 97.6% teacher satisfaction and 96.8% student satisfaction. In summary, the facial recognition model constructed by this paper has good recognition performance and can provide effective technical support for the construction of smart libraries.
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