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Record W4408142202 · doi:10.1093/heapro/daaf010

Fostering early adolescent health and planetary well-being through nature: a Delphi study on nature-based literacy

2025· article· en· W4408142202 on OpenAlexaff
Michelle Barrette, Patti‐Jean Naylor, Frédérick M. E. Grouzet, Nevin J. Harper

Bibliographic record

VenueHealth Promotion International · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsHealth literacyDelphi methodLiteracyPsychologyDevelopmental psychologyEnvironmental healthMedicinePolitical sciencePedagogyComputer scienceHealth care

Abstract

fetched live from OpenAlex

Becoming nature 'literate' may promote nature engagement in early adolescence, supporting both health and well-being, while nurturing a sense of environmental stewardship. This study aimed to advance the formative research necessary for the development of a nature-based literacy framework including a set of indicators specific to its measurement during early adolescence. An e-Delphi study design was used with an expert panel comprised of international academics (n = 18) and practitioners (n = 8). After three rounds, experts agreed key components of nature-based literacy were knowledge, competence, confidence, motivation, experience, connection, and stewardship. The nature-based literacy framework comprehensively represents the intrapersonal factors and related indicators that influence nature engagement, particularly during early adolescence. This will support the development of interventions designed to enhance early adolescent health outcomes, strengthen their connection to nature, and inspire them to value and protect the natural world.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.670

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 studies0.0000.000
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 head0.018
GPT teacher head0.364
Teacher spread0.346 · 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.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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