Exploring the Experiences of Autistic Graduate Women in Navigating the World of Work
Bibliographic record
Abstract
International research highlights poor prospects for autistic individuals in gaining and maintaining work (Baldwin et al., 2014; Harvery et al., 2021; Hendricks, 2010; Maslahati et al., 2022; Scott et al., 2019). Participation and graduation from higher education is said to increase the likelihood that autistic individuals will gain employment and experience higher paid salaries for their work (Migliore et al., 2012, Ohl et al., 2017; Rast et al., 2020). However, literature indicates that autistic graduates continue to remain less likely to be employed compared to graduates with other disabilities (Association of Graduate Careers Advisory Services, 2019) and far below the rates for the population in general. While there is some literature examining the trajectories of autistic adults, autistic women are under-represented within this (Leedham et al., 2020; Taylor & DaWalt, 2020). Research specific to the area of work and autism is either dominated by men (Baldwin et al., 2014; Griffith et al., 2012; Hayward et al., 2019) or the findings are not disaggregated by gender (Taylor & DaWalt, 2020). There is a lack of studies specifically relating to autistic women at work (Gemma, 2021; Hayward et al., 2018b) and even more so for autistic women who have successfully graduated from higher education. This study aims to bridge this gap by providing an in-depth understanding of the experiences and journeys of autistic graduate women in navigating the world of work. A narrative inquiry methodology was chosen to centralise the voices of the women (Polkinghorne, 1988). The research methods were informed by a reference group which comprised of autistic women. Purposive and snowball sampling was utilised to recruit a sample of twelve autistic women (26 to 53 years) who had attended higher education in the past ten years. Multiple interviews were completed using an in-depth interviewing method guided by concepts of the Biographic-Narrative-Interpretive-Method (Wengraf, 2001). An object selected by the women was also used to support the women in the telling of their story. The analysis utilised Polkinghorne’s (1995) orientation to narrative inquiry, where an analysis of narratives was completed guided by Braun and Clarke’s reflexive thematic analysis process (Braun & Clarke, 2022). The findings highlight that the working lives of these women were varied, multifaceted and often fragmented, with experiences of unemployment, underemployment, and instability. Influencing factors on their work experience included their perceptions of difference, double empathy problems as they navigated the social demands of work, challenges within the sensory environment and managing disclosure. The women were actively engaged in employing a variety of strategies to counteract their feelings of difference, in order to ‘fit in’ and as a survival skill to navigate a neurotypical working world. This came at a cost to the women and evident across most of their narratives was their experience of autistic burnout. While the women did experience barriers, the study findings also identified factors that enabled success. These included a good job-fit, supportive employment programmes, disclosure, access to reasonable accommodations as well as support from family and colleagues. The women identified suggestions and advice for employers to incorporate into work structures and practices to support and enable autistic women to thrive at work and to change the narrative from disabling to enabling. The women identified that the narrative inquiry approach facilitated their voices to be heard, placing them at the forefront of the study, and this was important to them. This study provided an important insight into the experiences and work journeys of autistic graduate women from an Irish context broadening our understanding of how contextual factors within the working world can shape and influence the women’s working lives. The findings identify a need to hear directly from autistic women regarding both their strengths and how the work environment could support successful work experiences. It highlighted the necessity of shifting societal and employer attitudes towards understanding and appreciating diversity within the workplace and to employ neurodiversity perspectives within the world of work. There is a need to foreground the voices of autistic women given that they have experience-based expertise, which is crucial in understanding autism from a women’s perspective (Milton & Bracher, 2013).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".