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Record W4214925166 · doi:10.1177/00938548221078002

A Meta-Analysis of the Effects of Wilderness Therapy on Delinquent Behaviors Among Youth

2022· article· en· W4214925166 on OpenAlexaff
Natalie Beck, Jennifer S. Wong

Bibliographic record

VenueCriminal Justice and Behavior · 2022
Typearticle
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsJuvenile delinquencyMeta-analysisWildernessModerationPsychologyClinical psychologyPoison controlHuman factors and ergonomicsInjury preventionDevelopmental psychologyMedicineSocial psychologyEnvironmental healthEcology

Abstract

fetched live from OpenAlex

The purpose of the present meta-analysis was to determine the effectiveness of wilderness therapy in addressing youth delinquency. A systematic review of the literature was conducted using 27 electronic databases and numerous gray literature sources, surveying literature published from 1990 to 2020. The search identified 189 potential studies for inclusion, resulting in a final study pool of 11 studies contributing 14 effect sizes from a total sample of 1,874 treatment youths. Both self-reported delinquency and caregiver-reported delinquency were examined using separate random-effects models. Pooled analyses yielded large, positive, and significant effects of 0.832 and 1.054 respectively, indicating that wilderness therapy is potentially an effective tool for addressing delinquent behaviors among youth. Limitations of the study include a lack of moderator analyses due to the small sample sizes. Wilderness therapy is a promising form of diversion programming and further investigation into this treatment modality is warranted.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.108
GPT teacher head0.376
Teacher spread0.269 · 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.

Study designQualitative
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

Citations17
Published2022
Admission routes1
Has abstractyes

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