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Record W4406153640 · doi:10.3390/bs15010054

Does Caregiver Engagement Predict Outcomes of Adolescent Wilderness Therapy?

2025· article· en· W4406153640 on OpenAlexaff
Joanna E. Bettmann, Naomi Martinez Gutierrez, Anna Jolley, Laura Mills

Bibliographic record

VenueBehavioral Sciences · 2025
Typearticle
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsYork University
Fundersnot available
KeywordsWildernessPsychologyPsychotherapist

Abstract

fetched live from OpenAlex

Existing research shows some links between wilderness therapy outcomes and familial functioning. However, wilderness therapy programs do not agree on what kind of caregiver involvement is required to improve adolescent program outcomes, nor has research examined different types of family engagement and their impact on adolescent treatment outcomes. Thus, the present study explored the research question: Does caregiver engagement in adolescent wilderness therapy foster improved outcomes? The study sample consisted of 4067 adolescent wilderness therapy clients from 12 different wilderness therapy programs. Using standardized measures and multilevel structural equation modeling, the authors found that caregiver program participation significantly predicted adolescent mental health outcomes of the program, suggesting that the more caregivers were involved in family interventions during the program, the more likely their adolescent child was to improve in the program. The study also found that greater caregiver effort predicted greater mean change in adolescent mental health outcomes of wilderness therapy. This study suggests the importance of enhancing familial interventions in adolescents' wilderness therapy programs in order to improve adolescent outcomes. Given findings from this study, wilderness therapy programs should consider expanding the ways that they involve families in treatment in order to optimize adolescent outcomes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.070
GPT teacher head0.435
Teacher spread0.365 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2025
Admission routes1
Has abstractyes

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