Exploring Working Women’s Experience With Mentorship in The Construction Trades: A Qualitative Analysis
Bibliographic record
Abstract
The construction industry remains predominantly male-dominated, where women face numerous challenges in entering and advancing in their construction careers in the trades. The construction trades have not been known for its welcoming attitude towards women entering the construction trades, with women constituting only 4% of the construction craft workforce. Mentorship in the construction trades is a well-regarded tool used to help new construction trades apprentices learn their craft and guide their careers. While research on issues related to mentorship in construction is prevalent in existing literature, no research has directly examined the perspective of working women regarding their experiences with mentorship in the construction trades. The authors of this study organized focus groups comprising women employed in construction crafts to capture their insights and experiences within the field. Across 29 sessions, 176 women participated, with each group consisting of 5-8 women. They were specifically questioned about their encounters with mentorship within the construction crafts domain. These focus group participants are from both the United States and Canada and have worked in diverse construction settings, including industrial and commercial sites. The purpose of this paper is to understand the perspective of working female craft professionals in the construction trades regarding their experience with mentorship. The focus group interviews underwent recording and transcription, after which a qualitative thematic content analysis was conducted on the transcripts. Key findings of this study show that women were often well-trained and pushed to learn by their mentors. Moreover, women were given advice and support regarding their careers. Still, many women indicated that they only had male mentors, highlighting the need for more women to fill such roles to help and guide new women in the construction trades.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".