“My Favourite Self:” A Retrospective Analysis of an Outdoor Orientation Program
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".