Gender Equality Training for Students in Higher Education: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: The principles of gender equality are integral to the goals, targets, and indicators of all sustainable development goals. Higher education institutes can be powerful agents for promoting gender equality, diversity, and inclusion not only in the higher education context but also in society as a whole. To address and overcome gender inequality in the higher education environment, experts posit that change needs to occur from day 1 of the student's academic experience. To this end, training is required. A preliminary review of the literature indicates that multiple gender equality-based training programs or initiatives for students have been designed and evaluated in second and third-level education settings. Examples of educational activities undertaken include delivery of didactic teaching, participation in a face-to-face collaboration project, site visits, case studies, and coaching. Yet, our initial search indicated that, to date, a comprehensive review collating the available evidence on gender equality training for third-level students has not yet been carried out. OBJECTIVE: Our review seeks to identify and explore the existing literature on gender equality training interventions for third-level students, with a particular emphasis on training content, methodology, and outcome evaluation. METHODS: This scoping review will be structured using the Arskey and O'Malley's 5-stage framework and will consider empirical research and other relevant published works that address gender equality training. Systematic searches will be carried out in 6 research databases and the gray literature using key search terms. Inclusion and exclusion criteria have been defined, and a data charting tool created to methodically extract information from selected literature. The free web software Rayyan will be used for primary screening where each reference will be screened in duplicate first by title, then abstract, and finally by full text. RESULTS: This review forms part of the LIBRA (Balance) study and has received peer-reviewed grant funding from the Irish Higher Education Authority. LIBRA aims to use simulation-based education to develop a gender equality leadership training program for student leaders in higher education. The findings will be summarized in tabular form, and a narrative synthesis produced to inform curriculum development. CONCLUSIONS: This review seeks to inform curriculum design by reporting on the gender equality-enabling skills and leadership skills necessary to foster gender equality. This paper should inform recommendations for training and catalyze future research in this rapidly evolving area. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/44584.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.096 | 0.090 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.109 | 0.022 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".