Risk, Resilience and Outdoor Programmes for At-risk Children
Bibliographic record
Abstract
Summary: In this article we explore a continuum of interventions that offer at-risk children and youth experience in outdoor wilderness environments. Though these experiences are thought to enhance the well-being of participants, the mechanisms by which programming in natural environments promotes health have been poorly understood. In this article we discuss different types of outdoor programmes in which social workers and allied professionals participate, linking programme goals and outcomes to research on mitigating risk and promoting resilience in at-risk populations. Findings: Findings are based on qualitative data gathered as part of regular programme evaluations and a separate study of 14 participants’ reactions to programming. Results show favourable outcomes in terms of relationship building and a sense of spirituality and purpose, though there was little increased awareness of environmental issues. Follow-up and support after programming helped to reinforce changes made during the outdoor experience. Applications:These case studies, combined with a review of the literature, provide the basis for a theoretically sound approach to understanding the health-enhancing mechanisms that operate through outdoor experience-based education and treatment. Similarities between the gains made in well-being and resilience are highlighted.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".