A content analysis of Québec’s K-11 Sexuality Education program using UNESCO guidelines
Bibliographic record
Abstract
The Sexuality Education program in Québec has been mandated across all schools in the province, however its English-language content had not yet been evaluated in the context of evidence-based practices. The present study used content analysis to explore the efficacy of Québec’s Sexuality Education content by determining how well it follows established guidelines. This research was grounded in a constructivist Intersectionality-Based Policy Analysis theoretical approach, rooted in critical policy studies. English-language Sexuality Education program documents were compared with UNESCO’s International technical guidance on sexuality education. A framework analysis approach to data analysis allowed themes to emerge from the data inductively while also providing the opportunity for UNESCO’s themes to map deductively onto the existing program. Analysis of Québec’s program revealed a lack of overall specificity and depth among 6 of the 8 learning themes. Of UNESCO's recommendations for comprehensive sexual education content development, 7 of 10 recommendations were not met with sufficient detail. Thorough skills-based content such as communication, decision-making, media literacy, and navigating services were notably absent, especially within an intersectional framework. These findings demonstrate a need for more detailed and intersectional Sexuality Education program content, especially while teacher training in the province is still in the process of being implemented. More research is needed to contextualize the implications of the findings with regard to the program's implementation and evaluation
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".