Large-scale rollout of extension training in Bangladesh: Challenges and opportunities for gender-inclusive participation
PLoS ONE, ISSN: 1932-6203, Vol: 17, Issue: 7 July, Page: e0270662
2022
- 11Citations
- 39Captures
- 1Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations11
- Citation Indexes9
- Policy Citations2
- 2
- Captures39
- Readers39
- 39
- Mentions1
- News Mentions1
- 1
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Article Description
Despite the recognized importance of women’s participation in agricultural extension services, research continues to show inequalities in women’s participation. Emerging capacities for conducting large-scale extension training using information and communication technologies (ICTs) now afford opportunities for generating the rich datasets needed to analyze situational factors that affect women’s participation. Data was recorded from 1,070 video-based agricultural extension training events (131,073 farmers) in four Administrative Divisions of Bangladesh (Rangpur, Dhaka, Khulna, and Rajshahi). The study analyzed the effect of gender of the trainer, time of the day, day of the week, month of the year, Bangladesh Administrative Division, and venue type on (1) the expected number of extension event attendees and (2) the odds of females attending the event conditioned on the total number of attendees. The study revealed strong gender specific training preferences. Several factors that increased total participation, decreased female attendance (e.g., male-led training event held after 3:30 pm in Rangpur). These findings highlight the dilemma faced by extension trainers seeking to maximize attendance at training events while avoiding exacerbating gender inequalities. The study concludes with a discussion of ways to mitigate gender exclusion in extension training by extending data collection processes, incorporating machine learning to understand gender preferences, and applying optimization theory to increase total participation while concurrently improving gender inclusivity.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85133705080&origin=inward; http://dx.doi.org/10.1371/journal.pone.0270662; http://www.ncbi.nlm.nih.gov/pubmed/35802660; https://dx.plos.org/10.1371/journal.pone.0270662; https://dx.doi.org/10.1371/journal.pone.0270662; https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0270662
Public Library of Science (PLoS)
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