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Record W3157146669

An outcome evaluation of a professional development opportunity focusing on sexuality education for early learning professionals

2021· article· en· W3157146669 on OpenAlexfundno aff
Alice-Simone Balter, Deborah Gores, Tricia van Rhijn, Jennifer Katz, Irene Kassies, Mary Margaret Gleason, Janelle Joseph

Bibliographic record

VenueThe Atrium (University of Guelph) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsnot available
FundersOffice of International Science and EngineeringUniversity of TorontoUniversity of Guelph
KeywordsHuman sexualityOutcome (game theory)Professional developmentSexuality educationMedical educationPedagogyPsychologyEngineering ethicsSex educationMedicineSociologyEngineeringGender studies
DOInot available

Abstract

fetched live from OpenAlex

This outcome evaluation assesses the impact of a one-day professional development opportunity in sexuality education for early learning professionals. A non-experimental pre-test/post-test research design evaluated the experiences of 28 participants. Thematic analysis and paired samples t-tests analyzed the perceived impacts and differences between pre- and post-test assessments. Positive changes were demonstrated in participants’ (a) perceptions of their daily practice, specifically increases in knowledge, comfort, and confidence in answering children’s questions about sexuality, and increased communication between staff and parents; and (b) preparedness to address sexuality in early learning settings. Recommendations for practice aim to increase professional capacity and provide the necessary support for early learning professionals.

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 imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.136
GPT teacher head0.389
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2021
Admission routes1
Has abstractyes

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