A systematic review of the literature: Gender-based violence in the construction and natural resources industry
Bibliographic record
Abstract
Gender-based violence (GBV) poses a significant concern in the construction and natural resources industries, where women, due to lower social status and integration, are at heightened risk. This systematic review aimed to identify the prevalence and experience of GBV in the construction and natural resources industries. A systematic search across databases including PubMed, OVID, Scopus, Web of Science, and CINAHL was conducted. The Risk of Bias Instrument for Cross-sectional Surveys of Attitudes and Practices by McMaster University and the Critical Appraisal of Qualitative Studies by the Center for Evidence Based Medicine at the University of Oxford were used to assess the studies included in the review. Six articles were included after full-text analysis. GBV was reported in the construction, mining, urban forestry, and arboriculture sectors. Workplace GBV was measured differently across the studies, and all studies examined more than one form of GBV. The main forms of GBV discussed in these studies were discrimination, sexual harassment, and sexism. The studies provided some insight for demographic factors that may or may not be associated with GBV, such as age, region of work, and number of years working in the industry. The review also suggests that workplace GBV has a negative impact on mental health and well-being outcomes, such as higher levels of stress and lower job satisfaction. The current research has not established the effectiveness of interventions, tools, or policies in these workplaces. Thus, additional research should include intervention studies that aim to minimize or prevent GBV in male-dominated workplaces. The current study can bring awareness and acknowledgement towards GBV in the workplace and highlight the importance of addressing it as this review outlines the negative consequences of GBV on mental health and well-being in these male-dominated industries.
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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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".