“They say we have a choice, but we don't”: A gendered reflection on work-family strategies and planning systems of atypical schedules within male-dominated occupations in Canada and Switzerland

Lefrançois, Mélanie; Probst, Isabelle

doi:10.1016/j.apergo.2019.103000

Cited by 5 publications

(2 citation statements)

References 43 publications

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“…Organizations can play an important role in encouraging more diverse workforces by understanding the different challenges facing male and female workers. For example, when workforces account for work–family balances in the scheduling of work patterns of workers in shifts and atypical work schedules, it can increase gender equity in domains that are more male‐dominant (Lefrançois & Probst, 2020). Yet, the foundations of many applications of teamwork assessments within the HFE domain have been on areas that are male‐dominant and have given little consideration for gender, or the different balance that females can bring to gender‐diverse teams.…”

Section: The Gender Data Gap At the Mesolevelmentioning

confidence: 99%

Section: The Gender Data Gap At the Mesolevelmentioning

confidence: 99%

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How can human factors close the gender data gap?

Parnell,

Plant

2023

Hum Ftrs & Erg Mfg Svc

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This commentary paper will describe how the discipline of human factors and ergonomics (HFE) can help to close the gender data gap, which is prevalent across many domains and arises due to a lack of data capturing female metrics and viewpoints. HFE is a domain‐independent discipline that seeks to understand human performance and well‐being with respect to the interactions that humans engage in and the environments that they inhabit. HFE therefore presents an opportunity to understand how gender influences human performance, effective design, social interactions, and environmental factors. This paper argues that a sociotechnical systems approach is essential when reviewing equality, diversity, and inclusivity issues, without which attempts to close the gender data gap will not go far enough. Following the sociotechnical systems approach in HFE, the micro‐, meso‐, and macro‐levels of system design with respect to closing the gender data gap are reviewed. We discuss these issues in relation to a case study example of a crash test dummy. A checklist approach for researchers is presented, which identifies key questions that prompt where gender should be considered in the research process across these levels of sociotechnical systems.

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Section: The Gender Data Gap At the Mesolevelmentioning

confidence: 99%

Section: The Gender Data Gap At the Mesolevelmentioning

confidence: 99%