scholarly journals Participatory health research under COVID-19 restrictions in Bauchi State, Nigeria: Feasibility of cellular teleconferencing for virtual discussions with community groups in a low-resource setting

2022 ◽  
Vol 8 ◽  
pp. 205520762110703
Author(s):  
Khalid Omer ◽  
Umaira Ansari ◽  
Amar Aziz ◽  
Khalid Hassan ◽  
Lami Aminati Bgeidam ◽  
...  

Introduction During the COVID-19 pandemic, researchers have used Internet-based applications to conduct virtual group meetings, but this is not feasible in low-resource settings. In a community health research project in Bauchi State, Nigeria, COVID-19 restrictions precluded planned face-to-face meetings with community groups. We tested the feasibility of using cellular teleconferencing for these meetings. Methods In an initial exercise, we used cellular teleconferencing to conduct six male and six female community focus group discussions. Informed by this experience, we conducted cellular teleconferences with 10 male and 10 female groups of community leaders, in different communities, to discuss progress with previously formulated action plans. Ahead of each teleconference call, a call coordinator contacted individual participants to seek consent and confirm availability. The coordinator connected the facilitator, the reporter, and the participants on each conference call, and audio-recorded the call. Each call lasted less than 1 h. Field notes and debriefing meetings with field teams supported the assessment of feasibility of the teleconference meetings. Results Cellular teleconferencing was feasible and inexpensive. Using multiple handsets at the base allowed more participants in a call. Guidelines for facilitators and participants developed after the initial meetings were helpful, as were reminder calls ahead of the meeting. Connecting women participants was challenging. Facilitators needed extra practice to support group interactions without eye contact and body language signals. Conclusions With careful preparation and training, cellular teleconferencing can be a feasible and inexpensive method of conducting group discussions in a low-resource setting.

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 93-LB
Author(s):  
EDDY JEAN BAPTISTE ◽  
PHILIPPE LARCO ◽  
MARIE-NANCY CHARLES LARCO ◽  
JULIA E. VON OETTINGEN ◽  
EDDLYS DUBOIS ◽  
...  

2021 ◽  
Vol 14 (4) ◽  
pp. e239250
Author(s):  
Vijay Anand Ismavel ◽  
Moloti Kichu ◽  
David Paul Hechhula ◽  
Rebecca Yanadi

We report a case of right paraduodenal hernia with strangulation of almost the entire small bowel at presentation. Since resection of all bowel of doubtful viability would have resulted in too little residual length to sustain life, a Bogota bag was fashioned using transparent plastic material from an urine drainage bag and the patient monitored intensively for 18 hours. At re-laparotomy, clear demarcation lines had formed with adequate length of viable bowel (100 cm) and resection with anastomosis was done with a good outcome on follow-up, 9 months after surgery. Our description of a rare cause of strangulated intestinal obstruction and a novel method of maximising length of viable bowel is reported for its successful outcome in a low-resource setting.


Author(s):  
Víctor Lopez-Lopez ◽  
Ana Morales ◽  
Elisa García-Vazquez ◽  
Miguel González ◽  
Quiteria Hernandez ◽  
...  

Author(s):  
Navin Kumar ◽  
Mukur Dipi Ray ◽  
D. N. Sharma ◽  
Rambha Pandey ◽  
Kanak Lata ◽  
...  

Author(s):  
Shumin Shi ◽  
Dan Luo ◽  
Xing Wu ◽  
Congjun Long ◽  
Heyan Huang

Dependency parsing is an important task for Natural Language Processing (NLP). However, a mature parser requires a large treebank for training, which is still extremely costly to create. Tibetan is a kind of extremely low-resource language for NLP, there is no available Tibetan dependency treebank, which is currently obtained by manual annotation. Furthermore, there are few related kinds of research on the construction of treebank. We propose a novel method of multi-level chunk-based syntactic parsing to complete constituent-to-dependency treebank conversion for Tibetan under scarce conditions. Our method mines more dependencies of Tibetan sentences, builds a high-quality Tibetan dependency tree corpus, and makes fuller use of the inherent laws of the language itself. We train the dependency parsing models on the dependency treebank obtained by the preliminary transformation. The model achieves 86.5% accuracy, 96% LAS, and 97.85% UAS, which exceeds the optimal results of existing conversion methods. The experimental results show that our method has the potential to use a low-resource setting, which means we not only solve the problem of scarce Tibetan dependency treebank but also avoid needless manual annotation. The method embodies the regularity of strong knowledge-guided linguistic analysis methods, which is of great significance to promote the research of Tibetan information processing.


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