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SNI/SNI Digital-Baghdad neurosurgery educational series
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Background:

Access to high-quality neurosurgery online learning is limited in low- and middle-income countries, and Iraq is part of this category. The need for collaboration and connection of people worldwide to exchange ideas and experiences in neurosurgery is a challenge. Surgical Neurology International® (SNI)/SNI Digital stimulated the establishment of the joint effort to bring the discussion about the best experiences in neurosurgery from the United States and Iraq together in an internet meeting format.

Methods:

An online survey was formulated and distributed to the attendees of the SNI-Baghdad neurosurgery meetings. The survey investigates the last 14 consecutive meetings for the period May/2021–April/2022. The online survey was designed utilizing Google Forms. The survey outline includes demographics, general aspects of the meetings, research aspects, surgical anatomy, neuroradiology, and capacity-building aspects. All these sections of the survey used a 5-point Likert scale.

Results:

The total number of participants was 84 out of 115 sent, with a response rate of 73.1%. The participants were diverse as they ranged from medical students to attending neurosurgeons. The male-to-female ratio was 1:1. Most attendees were from Baghdad (n = 66 [77.6%]) and the highest number of the responders was from the University of Baghdad (n = 46 [54.1%]).

Conclusion:

The targeted online collaborative meetings, SNI-Baghdad neurosurgery meeting as an example, give an insight into the effectiveness of such methods in providing a mutually beneficial educational experience between people of different parts of the world, as assessed by a survey initiated and performed by the attendees.

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Abstract

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Sustainable Urban Distribution of Educational Institutions and Population Density in Baghdad City Using Remote Sensing Techniques
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Impact of Smartphones Addiction upon Primary School Pupil's Achievements at Al-Rusafa Educational Directorate in Baghdad City
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Sun Feb 10 2019
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Educational Leadership
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Forecasting by Using the Optimal Time Series Method
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