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Cited by 168

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Using the Kaleidoscope Career Model to Understand the Changing Patterns of Women's Careers: Designing HRD Programs That Attract and Retain Women

Sullivan, Sherry E., Mainiero, Lisa·2008·Advances in Developing Human Resources

Abstract

The problem and the solution. This article explores how a new model of careers can be used to suggest human resource development (HRD) programs that better match the unique career patterns of women. Traditional career stage models, which form the basis of many modern HRD practices, were created by studying men's careers and do not fit the complexities of women's careers. The purpose of this article is to discuss the Kaleidoscope Career Model as a means of understanding the needs of women workers and how organizations can systematically fulfill those needs to gain a competitive advantage. In addition to HRD implications, we also discuss directions for future research.

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Metadata

Authors
Sullivan, S. E., & Mainiero, L.
Venue
Advances in Developing Human Resources
Published
2008-02
Volume
10
Issue
1
Pages
32-49
ISSN
  • 1523-4223 (ISSN-L)
  • 1552-3055
OpenAlex
W2021462478
Cited by
168
References
1
FWCI
14.82
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Using the Kaleidoscope Career Model to Understand the Changing Patterns of Women's Careers: Designing HRD Programs That Attract and Retain Women. https://app.cochat.ai/papers/10.1177/1523422307310110
The paper
Sullivan, S. E., & Mainiero, L. (2008). Using the Kaleidoscope Career Model to Understand the Changing Patterns of Women's Careers: Designing HRD Programs That Attract and Retain Women. Advances in Developing Human Resources. https://doi.org/10.1177/1523422307310110

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.