Analysis of vocational training for workers in new employment patterns based on spss statistics
Authors
Haoran Wang1, Xiaoyang Wang1, *, Yanzhi Duan2
1College of Marxism, Yunnan University of Finance and Economics, Kunming, China
2College of Marxism, Yuxi Normal University, Yuxi, China
*Corresponding author.
Email: 707149849@qq.com
Corresponding Author
Xiaoyang Wang
Available Online 27 October 2023.
- DOI
- 10.2991/978-94-6463-276-7_19How to use a DOI?
- Keywords
- Workers in new forms of employment; Vocational training; Dilemmas and responses
- Abstract
With the rapid development of the platform economy, the group of new employment pattern workers is expanding, and improving the welfare of new employment pattern workers is not only a realistic problem, but also a hot spot of academic concern. Based on the questionnaire survey data and analysed by using spss software, this study examines the education and training needs of new employment pattern workers in a city, and puts forward suggestions to improve the vocational training of this group through government guidance, community co-construction, and platform assistance.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Haoran Wang AU - Xiaoyang Wang AU - Yanzhi Duan PY - 2023 DA - 2023/10/27 TI - Analysis of vocational training for workers in new employment patterns based on spss statistics BT - Proceedings of the 2023 4th International Conference on Big Data and Social Sciences (ICBDSS 2023) PB - Atlantis Press SP - 167 EP - 177 SN - 2667-128X UR - https://doi.org/10.2991/978-94-6463-276-7_19 DO - 10.2991/978-94-6463-276-7_19 ID - Wang2023 ER -