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Record W4403500569 · doi:10.1177/10538259241290464

“Confident, Supportive, and Capable”: A Retrospective Analysis of an Outdoor Orientation Program

2024· article· en· W4403500569 on OpenAlexaff
Timothy S. O’Connell, Anna H. Lathrop, Kelly A. Pilato

Bibliographic record

VenueJournal of Experiential Education · 2024
Typearticle
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsBrock University
Fundersnot available
KeywordsOutdoor educationAdventure educationOrientation (vector space)PsychologyExperiential learningPedagogyApplied psychology

Abstract

fetched live from OpenAlex

Background: Initial effects of participation in outdoor orientation programs (OOPs) have been well-documented. However, there are few studies that examine longitudinal outcomes. Purpose: This study explored the long-term effects of engaging in an OOP. Methodology/Approach: This study employed the Most Significant Change technique and implemented a retrospective qualitative approach to understand the meaningful lessons participants learned as well as long-term outcomes they perceived as being important today. Alumni from an OOP (both participants and leaders) were questioned using semistructured interviews. Thematic analyses, using a constant comparison data analysis method, included open coding, focused coding, and axial coding. Findings/Conclusions: Major themes that emerged included understanding self, building community, and belonging to nature. Participants reported long-term impacts related to coping, stress relief, resiliency and thriving, relationship skills, sense of community, and environmental appreciation. The overarching theme of health and well-being was described as a long-term impact by participants. Implications: Results provide confirmation of positive long-term effects of OOPs and outdoor expeditions. Researchers and practitioners can use these results to better market programs to potential participants and parents, demonstrate the efficacy of OOPs to stakeholder groups, and inform staff training and program curricula.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.408
Teacher spread0.396 · 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
Published2024
Admission routes1
Has abstractyes

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