Working Women’s Recommendations to Recruit and Retain Women in The Construction Trades: A Qualitative Analysis
Bibliographic record
Abstract
As the construction industry continues to struggle with a decades-long labor shortage, there is a dire need to attract new workers. Historically, the construction industry, a heavily male-dominated industry, has not been known for its welcoming attitude towards women entering the construction trades, with women constituting only 4% of the construction craft workforce. Studies have highlighted that women encounter significant challenges and barriers when working and trying to join the construction industry. While research on issues related to women in construction is prevalent in existing literature, no research has directly examined the recommendations of working women to recruit and retain women into construction crafts. In this study, the authors conducted focus groups of women in construction crafts to gather their perspectives and their experiences regarding in construction crafts. A total of 176 women participated in 29 focus groups of 5-8 women each and were asked to recommend strategies to recruit and retain women into construction crafts. The focus group participants are from the United States and Canada and have worked in both industrial and commercial construction sites. The purpose of this paper is to understand the perspective of working female craft professionals in the construction industry regarding their recommendations to recruit and retain women in construction crafts. The focus group interviews were recorded and transcribed. A qualitative thematic content analysis was then performed on the transcripts. Key findings of this study show that women put a high emphasis on the importance of offering training opportunities to help recruit and retain women, as well as raising awareness, communicating the reality of the jobs without mincing words to potential recruits, highlighting the financial benefits of a career in the construction trades.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".