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Record W4381546995 · doi:10.22329/jtl.v17i1.7882

The Impact of COVID-19 on Instruction for and the Implementation of Quebec’s Sexual Health Curriculum

2023· article· en· W4381546995 on OpenAlexaffvenueabout
Enoch Leung, Katja Kathol, Tara Flanagan

Bibliographic record

VenueJournal of Teaching and Learning · 2023
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsCurriculumPandemicCoronavirus disease 2019 (COVID-19)CLARITYPsychologyMedical educationReproductive healthQualitative researchHuman sexualityPedagogyMathematics educationMedicineSociology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.069
GPT teacher head0.532
Teacher spread0.463 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes3
Has abstractyes

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