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Record W4390746079 · doi:10.1177/10538259231226389

“My Favourite Self:” A Retrospective Analysis of an Outdoor Orientation Program

2024· article· en· W4390746079 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
KeywordsThrivingThematic analysisPsychologyQualitative researchCoding (social sciences)Coping (psychology)Applied psychologySocial psychologyMedical educationSociologyMedicineSocial scienceClinical psychology

Abstract

fetched live from OpenAlex

Background: The short-term impacts of outdoor orientation programs (OOPs) have been documented in the literature for close to 40 years. While there is a fair amount of research examining the immediate effects of OOPs, there are relatively few studies exploring long-term impacts. Purpose: This study examined the important longitudinal “lessons learned” from participating in an OOP. Methodology/Approach: This study utilized a retrospective qualitative approach and employed the Most Significant Change technique to understand meaningful lessons learned. Alumni from an OOP participated in semi-structured interviews. Thematic analyses included open coding, focused coding, and axial coding. Findings/Conclusions: Primary themes that emerged from the coding process included community and social connections, mental health and well-being and environmental appreciation and value of nature. Participants reported learning valuable lessons related to community building, coping, stress relief, resiliency and thriving, and connection with nature. Implications: Results provide evidence supporting positive long-term effects of OOPs. A particular highlight is how participants noted the OOP helped shape their “favourite self” years after their university experience. Researchers and practitioners can use these results to inform OOP curricula and to include in program marketing and lobbying efforts.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
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
Bibliometrics0.0010.002
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 head0.011
GPT teacher head0.412
Teacher spread0.401 · 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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