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Record W4408361914 · doi:10.1007/s41297-025-00304-y

Examining three primary school curricula for their ability to promote health literacy development

2025· article· en· W4408361914 on OpenAlexaboutno aff
Nenagh Kemp, Vaughan Cruickshank, Joy Kemp, Rosie Nash

Bibliographic record

VenueCurriculum Perspectives · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
FundersUniversity of Tasmania
KeywordsCurriculumCurriculum studiesLiteracyPedagogyMathematics educationPrimary (astronomy)SociologyPsychology

Abstract

fetched live from OpenAlex

Abstract Primary schools have been identified as key settings in which to develop childhood health literacy (HL). However, little is known about how well existing curricula promote HL development amongst primary school aged children. The purpose of this paper was to analyse the health and physical education curricula of Australia, British Columbia (Canada), and New Zealand. Content analysis underpinned by Nutbeam’s Levels of HL (functional, interactive, critical) and Hjelm’s Dimensions of Health was used to analyse the three curricula. Similar trends were found between all three curricula, with the greatest emphasis placed on social health and physical health, and less emphasis on emotional and spiritual health. Consistent with other research, learning descriptors were found to most commonly relate to interactive HL, with critical HL occurring the least frequently. Findings from this study may guide the development of future iterations of each curriculum. Further, this paper provides an example of how other curricula can be analysed for their ability to comprehensively promote HL development.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
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 head0.051
GPT teacher head0.438
Teacher spread0.388 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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