Let's talk about sex: the relevance of Québec's 2018 sexuality education curriculum reform for today's young people
Bibliographic record
Abstract
Between 2005 and 2018, Québec high schools operated without a mandatory and consistent sexuality education curriculum, resulting in significant gaps in young people's sexual and reproductive health and rights (SRHR) education. In response, the province introduced a compulsory curriculum in 2018 aimed at delivering comprehensive, age-appropriate content across primary and secondary schools. This thesis critically examines the relevance and adequacy of the 2018 reform through the perspectives of young adults who attended high school prior to its implementation. Guided by asset-based and feminist pedagogical frameworks, this qualitative study draws on semi-structured narrative interviews with eleven young adults and a document analysis of Québec Education Program (QEP) sexuality education materials. Findings reveal that participants’ formal SRHR education was often incomplete, heteronormative, shame-based, and inconsistently delivered, with long-term impacts on sexual health literacy, autonomy, and well-being. While the 2018 curriculum represents important progress, gaps remain in areas such as intersectionality, identity affirmation, and pleasure-based learning. The study concludes that further reforms must prioritize teacher education, diverse representation, and peer-led, relational strategies to better meet the evolving SRHR needs of youth in Québec
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".