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Record W7056074488

Elementary School Teachers’ Lived Experiences of Teaching Nutrition: A Qualitative Study

2024· article· en· W7056074488 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2024
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsnot available
Fundersnot available
KeywordsQualitative researchLived experiencePerceptionLifelong learningFace (sociological concept)Semi-structured interviewNutrition EducationResource (disambiguation)
DOInot available

Abstract

fetched live from OpenAlex

Early nutrition education is crucial for lifelong health and learning (Cusick et al., 2016). Elementary schools and teachers play a significant role in shaping children's understanding of nutrition (Cotton et al., 2020). Yet, there's limited research on teachers' experiences teaching nutrition to elementary students and how it affects their well-being. This qualitative study examines 13 Ontario-based teachers' experiences in delivering nutrition education. Interviews revealed themes such as teachers' perceptions of their role and secondly challenges and facilitators in teaching nutrition, including time constraints, resource scarcity, body image, and cultural differences. These findings offer insights into the complexities teachers face in imparting this vital skill to children. The research aims to support teachers and promote positive change in education, fostering classrooms of positivity, impartiality, and acceptance. Ultimately, it strives for each student to embrace their dietary preferences, understand their culinary heritage, and envision their future selves, fostering inclusivity and enriching learning environments.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.129
GPT teacher head0.375
Teacher spread0.246 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

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