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Record W2991717359 · doi:10.1177/0093854819891457

Evaluating the Effectiveness of Mindfulness-Based Interventions in Incarcerated Populations: A Meta-Analysis

2019· article· en· W2991717359 on OpenAlexaff
Megan Per, Christina Spinelli, Isabel Sadowski, Emma Schmelefske, Leena Anand, Bassam Khoury

Bibliographic record

VenueCriminal Justice and Behavior · 2019
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsMcGill University
Fundersnot available
KeywordsMindfulnessPsychological interventionClinical psychologyAnxietyRecidivismPsychologyMeta-analysisImpulsivityAngerPoison controlPsychiatryMedicine

Abstract

fetched live from OpenAlex

A growing body of research supports the use of mindfulness-based interventions (MBIs) in incarcerated populations; however, treatment effectiveness remains unclear. This meta-analysis of 22 studies ( N = 2,265, 75% male) quantified the effectiveness of MBIs in incarcerated populations on key psychological outcomes and criminogenic needs. Results from pre–post studies indicated MBIs had a small to moderate effect on all outcomes, but this was not supported by controlled studies. Studies with older participants, more females, and longer treatment length demonstrated slightly greater effects. Results from pre–post analyses demonstrated significant reductions in psychological outcomes (depression, anxiety, and stress) and criminogenic needs (impulsivity, self-dysregulation, anger, substance use behavior, and attitude). However, only depression and anxiety were significantly reduced in controlled studies. Although findings offer preliminary support of the efficacy of MBIs in targeting psychological health in incarcerated populations, further controlled studies are required to examine criminogenic outcomes and recidivism rates after treatment.

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)0.001
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: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.000

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 head0.315
GPT teacher head0.491
Teacher spread0.176 · 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 designMeta-analysis
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

Citations33
Published2019
Admission routes1
Has abstractyes

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