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Record W4389994586 · doi:10.1080/14729679.2023.2295842

Understanding older adults’ participation in outdoor adventure activities: a scoping review

2023· review· en· W4389994586 on OpenAlexaff
Emily Smith, Nicole Dalmer

Bibliographic record

VenueJournal of Adventure Education & Outdoor Learning · 2023
Typereview
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAdventureOutdoor educationAdventure educationPsychologyRecreationOutdoor activityGerontologyFocus groupMedical educationApplied psychologyMedicineSociologyPedagogyPolitical science

Abstract

fetched live from OpenAlex

Outdoor adventure activities are increasingly popular among older adults. We conducted a scoping review to examine trends in the scholarly literature on this topic. Several interdisciplinary databases were searched, and studies were independently screened for eligibility during two rounds of review (title and abstract, and full text). Our review included 34 peer-reviewed articles with the full text available in English that substantially described outdoor adventure programming for older adults, and/or older adults’ experiences of, or attitudes towards outdoor adventure activities. Results of this scoping review suggest that participation in outdoor adventure activities can contribute significantly to the wellbeing of older adults. However, due to strict alignment to the ideals of successful aging present in many articles, the potential for an ‘authentic aging’ lens to explore the diverse experiences of aging is discussed. Further research should focus on recruiting more diverse participants and attempt to uncover potential barriers and facilitators (physical, economic, geographic, etc.) to participation.

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)0.008
metaresearch head score (Gemma)0.035
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 score0.011
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0110.010
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.124
GPT teacher head0.472
Teacher spread0.348 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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