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Record W4396866429 · doi:10.1080/14729679.2024.2353163

Exploring the significance of early life outdoor experiences: a scoping review of retrospective outdoor methods

2024· review· en· W4396866429 on OpenAlexaff
Jonah D’Angelo, Stephen D. Ritchie, Simon Priest, Bruce Oddson, Dan Scott

Bibliographic record

VenueJournal of Adventure Education & Outdoor Learning · 2024
Typereview
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsLaurentian University
Fundersnot available
KeywordsAestheticsPsychologyArt

Abstract

fetched live from OpenAlex

Outdoor programs have shown holistic health benefits for participants, with recent evidence indicating that these benefits can extend long after the conclusion of the program. The methods employed in retrospective studies exploring these outcomes are diverse, leading to many different approaches. Furthermore, only a few studies reference a theoretical framework guiding the authors’ approach. The primary objectives of this review were to (1) identify the purposes and outcomes from retrospective studies related to outdoor experiences, (2) summarize the methodological characteristics, and (3) compile reported methodological limitations. A Peer Reviewed Electronic Search Strategy (PRESS) was employed to search four prominent databases; Yielding 5206 candidate studies, from which 31 met the inclusion criteria. Data analysis revealed that there were four main study purposes and 22 unique outcomes. Retrospective, longitudinal, and follow-up were the three main methodological designs, with methods exhibiting significant variation and diversity. This review concludes with five suggestions for future research.

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)0.019
metaresearch head score (Gemma)0.074
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.074
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0200.019
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.146
GPT teacher head0.493
Teacher spread0.347 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations3
Published2024
Admission routes1
Has abstractyes

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