A cross-national, cross-sectional study of women's retention and advancement in Information Technology (IT) and Engineering careers – Canada Report
Bibliographic record
Abstract
This report offers summary results from the Canada-phase of the Cross-National, Cross-Sectional Study of Women’s Retention and Advancement in Information Technology and Engineering Careers project. Women continue to be underrepresented in science, technology, engineering, and mathematic (STEM) fields. This research highlights factors that contribute to the retention and attrition rates of women working in engineering and information and communication technology (EICT) jobs across Canada. The primary objective of this study is to identify the impact of welfare state entitlements, job factors, and family/individual circumstances on women’s intent to stay or leave their jobs. Our findings suggest that job-related factors such as dissatisfaction with salary, few promotion opportunities, and long working hours have the biggest impact on job attrition. As well, emotional exhaustion from the interference of work and life, experiences of sexual harassment and sexual microaggressions, exclusion from peer networks, and a lack of institutional/organizational support can create toxic work environments that contribute to women’s decision to leave their jobs. Therefore, supportive workplaces that offer flexible work options to promote work-life balance, good pay, peer inclusion, and work autonomy can improve job retention. Improvements to welfare state entitlements including childcare, parental leave, elder care, and/or illness/injury leave may also reduce the pressures of work-life interference and improve the work-life balance of EICT women who continue to be primary caregivers. The respondents for the Canada survey also highlight the continued presence of gendered informal and formal networks that are male-dominated within EICT workplaces. It remains a challenge to find “good” mentors and mentors of diversity that can assist them with career advancement. Another objective of this study is to evaluate the impact and variation of these circumstances by employment sector and work type. We directly compared the experiences of women working in engineering to computer science and information technology (CSIT), as well as women working in the academic sector to the non-academic sector. Our findings indicate that there are pros and cons to working in each work area and/or sector. In the future, we will compare these results to similar surveys administered in Sweden and Germany to uncover potential similarities and differences in job attrition and retention. Overall, the statistical analysis demonstrates that, despite increased efforts to improve gender equity across STEM fields, gender inequalities, stereotypes, and biases remain problems within EICT in Canada, shaping women’s day to day workplace experiences across employment sectors. We would like to thank the Social Sciences and Humanities Research Council for their support and funding of this project.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".