Gender Equality Training for Students in Higher Education: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Despite recent improvements, gender inequality persists within the higher education sector, as evidenced by the proportionally greater number of student and academic leadership positions occupied by male students and staff. Gender equality education and training for students may help to develop awareness, knowledge, and skills among individual students, building capacity to address biases and accelerate culture change in higher education institutions. OBJECTIVE: We aimed to identify and explore the existing literature on gender equality training interventions for students in tertiary education, with a particular emphasis on training content, methodology, and outcome evaluation. METHODS: The 6-stage framework developed by Arskey and O'Malley was used to map the literature related to current best practice in gender equality training for students in higher education. Systematic database searches of peer-reviewed literature were carried out and 3142 titles, 333 abstracts, and 52 full-text articles were screened for eligibility with 14 (27%) articles selected for inclusion in this review. RESULTS: The selected studies detailed a range of pedagogical approaches, including didactic lectures, participatory and co-design workshops, reflective writing, and service-learning, with durations ranging from a single interaction to 1 year. Most articles reviewed did not explicitly state their study aims or research question, and the theoretical underpinnings were generally vaguely described. The longer-term impact of most interventions was unclear, as evaluation metrics seldom go beyond the level of adoption. CONCLUSIONS: This scoping review shows that the literature base for gender equality training for tertiary students lacks coherence, highlighting the need for further work to evaluate its impact. This work provides a foundation for developing training design recommendations.
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 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 | 0.022 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 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".