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Record W4407761301 · doi:10.18666/jorel-2025-12627

Nature-Based Programs and Youth Mental Health: What Do We Know and What Do We Need to Know?

2025· article· en· W4407761301 on OpenAlexaff
Simon Beames, Marte Bentzen, Solfrid Bratland‐Sanda, Nevin J. Harper, Kaye Richards

Bibliographic record

VenueJournal of Outdoor Recreation Education and Leadership · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsNeed to knowMental healthPsychologyComputer sciencePsychotherapistComputer security

Abstract

fetched live from OpenAlex

This paper provides commentary on two societal concerns: increasing levels of youth mental health issues, and the lack of large-scaled, widely implemented methods to respond to this concern. The authors explicate the twin problems of worsening youth mental health and the lack of large scale research examining approaches to tackle the trend, and focus on the role of nature-based programs in addressing these phenomena. Suggestions are provided for research designs which may yield vital knowledge on suitable methodologies and appropriate theoretical frameworks which can be harnessed to improve youth mental health through access to activities in natural 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.766
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.071
GPT teacher head0.362
Teacher spread0.291 · 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
Published2025
Admission routes1
Has abstractyes

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