The Impact of COVID-19 on Instruction for and the Implementation of Quebec’s Sexual Health Curriculum
Bibliographic record
Abstract
In March 2020, the COVID-19 pandemic necessitated school closures across Quebec. Educators shifted to online learning and complied with COVID-19 safety measures for in-person teaching, impacting the implementation of Quebec’s Sexuality Education program. Drawing on responses from a sample of 165 in-service teachers working in English school boards across Quebec, this study discusses the challenges that characterized teaching sexual health education (SHE) during the COVID-19 pandemic. The data analyzed in this study consist of teachers’ responses to one qualitative question: How has the COVID-19 situation affected your teaching and incorporation of Quebec’s comprehensive sexual health education curriculum in your classroom? The results indicate that educators taught less SHE during the COVID-19 pandemic due to a lack of time and other core curriculum subjects taking precedence. Other challenges were present, including a lack of clarity from school administrators on how SHE should be implemented, reduced ability to supplement SHE classes with guest speakers, difficulty facilitating discussions due to students’ home environments, and decreased student engagement. Despite these barriers, teachers felt that teaching SHE during the COVID-19 pandemic was important and expressed the need for more pedagogical development and training opportunities to improve SHE both online and in person.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".