A predictive psychological health model for women performance at work

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Authors

Femi Johnson¹ , Oluwafolake Ojo¹ , Adejimi Alaba¹

1. FEDERAL UNIVERSITY OF AGRICULTURE, ABEOKUTA

Conference / event

1st Annual ACM Celebration of Women in Computing in Africa: AfricaCWIC 2022, May 2022 (Virtual)

Poster summary

Employers have faced a significant issue in choosing employees for particular work roles. As a result of the perception that certain job positions are more commonly fit for one gender than another that has developed over time, more employees are being placed in incorrect job roles without taking into account some underlying elements that may affect their productivity. As an alternative, we only provide an artificially intelligent model with a high rate of prediction to assist in the identification of these elements and address the difficulties employers face in the selection and placement of workers for job tasks.

Keywords

Work performance, Ratings, Artificial intelligence

Research areas

Computer and Information Science , Medical Imaging, Bioinformatics and Genomics

References

1. Braddy, P. W., Sturm, R. E., Atwater, L., & McKee, R. A. (2020). Gender Bias Still Plagues the Workplace: Looking at Derailment Risk and Performance With Self–Other Ratings.
2. Frin, S.; Raihan,T.; Uddin, A.I.;Uddin, M.A. (2022). Predicting Innovative Work Behaviour in an Interactive Mechanism.
3. Vonderlin R, Schmidt B, Müller G,Biermann M,Bohus M and Lyssenko L., (2021).Health-Oriented Leadership and Mental Health From Supervisor and Employee Perspectives: A Multilevel and Multisource Approach.

Funding

No data provided

Supplemental files

No data provided

Additional information

Competing interests

No competing interests were disclosed.

Data availability statement

The datasets generated during and / or analyzed during the current study are available from the corresponding author on reasonable request.

Creative Commons license

Copyright © 2023 Johnson et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.