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Record W2753706509

Let’s Talk About Sex: Independent School Teachers’ Experiences Implementing Sexual Health

2017· article· en· W2753706509 on OpenAlexaboutno aff
Rachel A. Burton

Bibliographic record

VenueTSpace (University of Toronto) · 2017
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Health and Behaviors
Canadian institutionsnot available
Fundersnot available
KeywordsSex educationReproductive healthPsychologyPedagogyHuman sexualityMathematics educationSociologyGender studiesDemographyPopulation
DOInot available

Abstract

fetched live from OpenAlex

This research project focuses on Toronto independent school teachers’ experiences implementing the updated Health and Physical Education (HPE) curriculum, with a specific focus on sexual health education. While there is some existing literature on the experiences of public school teachers’ experiences, there was a gap in the literature concerning independent school teachers’ experiences. Using qualitative methods, five HPE teachers from the Conference of Independent Teachers participated in semi-structured interviews. Data from these interviews were coded and analyzed to reveal moments of convergence and divergence with the literature reviewed. Findings from this study indicate that independent school teachers perceive their experiences implementing sexual health education as different than public school teachers because of the additional supports they receive as a result of working at an independent school. These supports include the freedom to change and modify physical spaces, the ability to work with a large department both in and out of the classroom, and the support they receive from parents. Overall, these findings suggest independent school teachers perceive that they receive additional supports than public school teachers and thus have a positive experience implementing sexual health education.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0240.000

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 head0.080
GPT teacher head0.417
Teacher spread0.337 · 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
Published2017
Admission routes1
Has abstractyes

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