e-Delphi in the outdoors: Stakeholder contributions to the development of a wellbeing-focused outdoor adventure education intervention program
Bibliographic record
Abstract
Issue Addressed Outdoor adventure education (OAE) (programs involving outdoor activities such as rock climbing or white-water canoeing) that participants perceive as risky, conducted in a social support setting, can be utilised by practitioners to elicit changes in educational and psychosocial outcomes to support participant adolescent wellbeing. Methods This study garnered the opinions of an expert OAE panel on the content of future programs aiming to impact adolescent wellbeing. The panel consisted of local (Western Australia, n = 7), national (Australia, n = 4), and international (Canada, Germany, New Zealand, United Kingdom, United States, n = 7) experts. A two-round, mixed-methods Delphi approach was employed. Extensive formative work led to the development of a series of open-ended questions requiring qualitative responses for round one. Panellists were also asked to respond to 17 statements using Likert scales in the second round. Results After analysis, a consensus was reached for all statements, with five statements having high consensus and being considered important by panellists. Conclusions The statement ‘Equity for all participants requires flexible delivery and facilitation’ had the highest level of agreement amongst panellists. Connections, authentic experiences, and equitable experiences developed as key themes. So What? Future OAE interventions focused on wellbeing impact could use the findings of this research as a basis for program design.
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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 | 0.089 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".