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Record W2802927947 · doi:10.5206/uwomj.v87i1.1892

Mindfulness-based cognitive therapy for young adults with cancer

2018· article· en· W2802927947 on OpenAlexvenueno aff
Amanda Roth, Rinat Nissim, Mary Elliott

Bibliographic record

VenueUniversity of Western Ontario Medical Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsMindfulness-based cognitive therapyMindfulnessAnxietyClinical psychologyPsychologyPsychological interventionPopulationCognitive therapyGeneralizability theoryPsychotherapistCognitionIntervention (counseling)MedicinePsychiatryDevelopmental psychology

Abstract

fetched live from OpenAlex

Introduction: Cancer diagnosis and treatment frequently involves physical and psychological symptoms, including anxiety, depression, fatigue, and sleep disturbances. The young adult population with cancer face unique struggles including poignancy in relation to self-concept, identity formation, independence, role development, loss of independence, and time away from school and peers. Mindfulness-based interventions are increasingly being evaluated for individuals with a cancer diagnosis. Mindfulness-Based Cognitive Therapy (MBCT) combines Mindfulness-Based Stress Reduction with aspects of cognitive behavioural therapy. This paper aims to briefly describe MBCT and its benefits and challenges in the young adult population with cancer.
 Method: An analysis of themes was conducted of post-intervention semi-structured interviews that were conducted with a subsample of 14 participants to gain more detailed information regarding their perception.
 Findings: Participants reported positive transformations including in how they cope.
 Conclusions: Although a small sample size limits its generalizability, this study provides further evidence that MBCT can be successful in treating psychological symptoms in young adults with cancer.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score0.998

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.285
Teacher spread0.264 · 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 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

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

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