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Record W2754705845 · doi:10.1038/bjc.2017.326

Group interventions to reduce emotional distress and fatigue in breast cancer patients: a 9-month follow-up pragmatic trial

2017· article· en· W2754705845 on OpenAlexaff
Charlotte Grégoire, Isabelle Bragard, Guy Jérusalem, Anne-Marie Étienne, Philippe Coucke, Gilles Dupuis, Dominique Lanctôt, Marie-Élisabeth Faymonville

Bibliographic record

VenueBritish Journal of Cancer · 2017
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversité de MontréalUniversité du Québec à MontréalHEC Montréal
FundersPlan National Cancer
KeywordsPsychological interventionAnxietyPsychosocialDistressBreast cancerMedicineQuality of life (healthcare)Physical therapyRandomized controlled trialClinical psychologyDepression (economics)CancerPsychiatryInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Long-term effects of psychosocial interventions to reduce emotional distress, sleep difficulties, and fatigue of breast cancer patients are rarely examined. We aim to assess the effectiveness of three group interventions, based on cognitive behavioural therapy (CBT), yoga, and self-hypnosis, in comparison to a control group at a 9-month follow-up. METHODS: A total of 123 patients chose to participate in one of the interventions. A control group was set up for those who agreed not to participate. Emotional distress, fatigue, and sleep quality were assessed before (T0) and after interventions (T1), and at 3-month (T2) and 9-month follow-ups (T3). RESULTS: Nine months after interventions, there was a decrease of anxiety (P=0.000), depression (P=0.000), and fatigue (P=0.002) in the hypnosis group, and a decrease of anxiety (P=0.024) in the yoga group. There were no significant improvements for all the investigated variables in the CBT and control groups. CONCLUSIONS: Our results showed that mind-body interventions seem to be an interesting psychological approach to improve the well-being of breast cancer patients. Further research is needed to improve the understanding of the mechanisms of action of such interventions and their long-term effects on quality of life.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score0.995

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)0.0000.000

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 head0.026
GPT teacher head0.341
Teacher spread0.315 · 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

Citations63
Published2017
Admission routes1
Has abstractyes

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