Teaching sexual consent to young people in education settings: a narrative systematic review
Bibliographic record
Abstract
Olivia Burton, Patrick Rawstornea, Lucy Watchirs-Smithb, Sally Nathana & Allison Carterbcd*a School of Population Health, Faculty of Medicine & Health, UNSW, Sydney, Australiab The Kirby Institute for Infection and Immunity in Society, UNSW, Sydney, Australiac Australian Human Rights Institute, UNSW, Sydney, Australiad Faculty of Health Sciences, Simon Fraser University, Burnaby, CanadaCONTACT Allison Carter acarter@kirby.unsw.edu.auABSTRACTThe recent outpouring of testimonies about teenage sexual assault has reinvigorated calls for improved education on sexual consent. Better understanding of the approach, content and delivery of these programmes is key to informing best practice. In this paper, we systematically searched for peer-reviewed articles on programmes in education settings for young people aged 15–29 that purport to teach sexual consent, with 18 meeting the inclusion criteria. Nearly all reviewed programmes were implemented in the USA (n = 16) in university settings (n = 15), with short-term duration (1–2-hour sessions), with varied facilitators and interactive teaching strategies. Thematic analysis identified four main approaches to sexual consent education, some of which were interwoven within programmes: risky behaviour, sex-positive, life skills, and socioculturally adapted. In line with existing research into best practice sex and relationship education, we recommend that consent education programmes take a sex-positive and whole-school approach, are interactive and inclusive, and facilitate critical analysis of how experiences of consensual and non-consensual sexual activity are connected to socio-structural forces within socio-cultural contexts. Future research should evaluate a larger number of programmes and ensure consistent measurement of programme outcomes, whilst taking account of complex social systems and their shifting influence on consent.
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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 | 0.013 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".