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Record W7110692404

A cross-national, cross-sectional study of women's retention and advancement in Information Technology (IT) and Engineering careers – Sweden Report

2025· other· en· W7110692404 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsAttritionPromotion (chess)Information technologyWelfareWork (physics)UnemploymentEmployee moraleEmployee retentionJob satisfaction
DOInot available

Abstract

fetched live from OpenAlex

This report offers summary results from the Sweden survey 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 project highlights factors that contribute to the retention and attrition rates of women working in engineering and information and communication technology (EICT) jobs across Canada, Sweden, and Germany. The primary objective of this project 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 from the Sweden survey suggest that job-related factors such as limited permanent positions, dissatisfaction with salary, few promotion opportunities, and hostile climate are the main reasons for respondents’ job attrition. Therefore, workplaces that offer job security, good pay, and supportive peers, leaders and management can improve job retention. Improvements to welfare state entitlements for students like increased study allowance, grants, and loans, and increases to benefits for illness/injury leave and unemployment may also reduce the pressures of work-life interference. The respondents for the Sweden survey did not support increasing child-related benefits or parental leave. 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. We also compared Sweden-born respondents to respondents born in other countries. Our findings indicate that broadly there is greater job retention within engineering than CSIT despite CSIT respondents expressing more career satisfaction and less workplace alienation. We also find that respondents born in Sweden have more career satisfaction and less workplace alienation compared to respondents born elsewhere. These differences highlight the need to take an intersectional approach to understanding women’s working experiences within EICT in Sweden. In the future, we will compare these results to similar surveys administered in Germany and Canada to uncover potential similarities and differences in job attrition and retention cross-nationally. 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 Sweden, 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.013
GPT teacher head0.241
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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