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Record W2520902226 · doi:10.2147/ahmt.s65820

Improving self-regulation in adolescents: current evidence for the role of mindfulness-based cognitive therapy

2016· review· en· W2520902226 on OpenAlexaff
Erica Sibinga, Nikeea Copeland‐Linder, Lindsey Webb, Ashley Shields, Carisa Perry‐Parrish

Bibliographic record

VenueAdolescent Health Medicine and Therapeutics · 2016
Typereview
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsMindfulnessRuminationMindfulness-based cognitive therapyMeditationAnxietyPsychotherapistCognitionCognitive therapyClinical psychologyMedicineStressorDepression (economics)Mindfulness meditationAcceptance and commitment therapyPsychologyPsychiatryIntervention (counseling)

Abstract

fetched live from OpenAlex

Mindfulness-based cognitive therapy (MBCT) was introduced in 1995 to address the problem of recurrent depression. MBCT is based on the notion that meditation helps individuals effectively deploy and regulate attention to effectively manage and treat a range of psychological symptoms, including emotional responses to stress, anxiety, and depression. Several studies demonstrate that mindfulness approaches can effectively reduce negative emotional reactions that result from and/or exacerbate psychiatric difficulties and exposure to stressors among children, adolescents, and their parents. Mindfulness may be particularly relevant for youth with maladaptive cognitive processes such as rumination. Clinical experience regarding the utility of mindfulness-based approaches, including MBCT, is being increasingly supported by empirical studies to optimize the effective treatment of youth with a range of challenging symptoms. This paper provides a description of MBCT, including mindfulness practices, theoretical mechanisms of action, and targeted review of studies in adolescents.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.189
GPT teacher head0.478
Teacher spread0.289 · 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 designOther design
Domainnot available
GenreReview

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

Citations61
Published2016
Admission routes1
Has abstractyes

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