Trait emotional intelligence, client symptoms, and predictive factors in wilderness therapy
Bibliographic record
Abstract
Background: Mental health issues and harmful substance use are problems that affect many Canadian youth. Wilderness therapy (WT) is a residential adventure-based therapy modality shown to have some success in treating these issues. Further research is needed regarding the ways that participants change, and if there are certain individuals that benefit more from this treatment than others. Purpose: The purpose of this study is to explore the changes in presenting problems and trait emotional intelligence of participants at one WT organization in Ontario, Canada. The working alliance - shown to have a positive impact on therapeutic treatment - along with sex and age, were examined to determine if these elements moderate outcomes. Methodology: Two separate samples were created from archival data provided by the participating organization. The first sample includes pre and post Youth Outcome Questionnaires (N=30, 14 to 18 year olds). The second sample includes pre and post Trait-Emotional Intelligence Questionnaires (N=68 youth, 16 to 20 year olds). All participants in both groups completed one Working Alliance Inventory post-WT. Descriptive statistics were calculated, paired t-tests were run, and Pearson correlation matrices and visualizations were created. Findings/Conclusions: Findings indicate that older male individuals report greater reductions in presenting problems as a result of their participation in WT. Trait emotional intelligence did not seem to change, and the working alliance did not seem to moderate any of these outcomes.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".