Exploring the influence of context in a community-based facilitation intervention focusing on neonatal health and survival in Vietnam : a qualitative study

Background In the Neonatal health – Knowledge into Practice (NeoKIP) trial in Vietnam, local stakeholder groups, supported by trained laywomen acting as facilitators, promoted knowledge translation (KT) resulting in decreased neonatal mortality. In general, as well as in the community-based NeoKIP trial, there is a need to further understand how context influences KT interventions in low- and middle-income countries (LMICs). Thus, the objective of this study was to explore the influence of context on the facilitation process in the NeoKIP intervention. Methods A secondary content analysis was performed on 16 Focus Group Discussions with facilitators and participants of the stakeholder groups, applying an inductive approach to the content on context through naïve understanding and structured analysis. Results The three main-categories of context found to influence the facilitation process in the NeoKIP intervention were: (1) Support and collaboration of local authorities and other communal stakeholders; (2) Incentives to, and motivation of, participants; and (3) Low health care coverage and utilization. In particular, the role of local authorities in a KT intervention was recognized as important. Also, while project participants expected financial incentives, non-financial benefits such as individual learning were considered to balance the lack of reimbursement in the NeoKIP intervention. Further, project participants recognized the need to acknowledge the needs of disadvantaged groups. Conclusions This study provides insight for further understanding of the influence of contextual aspects to improve effects of a KT intervention in Vietnam. We suggest that future KT interventions should apply strategies to improve local authorities’ engagement, to identify and communicate non-financial incentives, and to make disadvantaged groups a priority. Further studies to evaluate the contextual aspects in KT interventions in LMICs are also needed.

Machine vision for the automatic classification of images acquired from Non-destructive tests

This project is based on Artificial Intelligence (A.I) and Digital Image processing (I.P) for automatic condition monitoring of sleepers in the railway track. Rail inspection is a very important task in railway maintenance for traffic safety issues and in preventing dangerous situations. Monitoring railway track infrastructure is an important aspect in which the periodical inspection of rail rolling plane is required.Up to the present days the inspection of the railroad is operated manually by trained personnel. A human operator walks along the railway track searching for sleeper anomalies. This monitoring way is not more acceptable for its slowness and subjectivity. Hence, it is desired to automate such intuitive human skills for the development of more robust and reliable testing methods. Images of wooden sleepers have been used as data for my project. The aim of this project is to present a vision based technique for inspecting railway sleepers (wooden planks under the railway track) by automatic interpretation of Non Destructive Test (NDT) data using A.I. techniques in determining the results of inspection.