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Effects of mindfulness-based stress reduction training on negative emotions in elderly patients with chronic heart failure

2018· article· en· W3029701973 on OpenAlexaboutno aff
Hongxia Zhao, Yuan Yuan, Changying Chen

Bibliographic record

VenueZhonghua xiandai huli zazhi · 2018
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsMindfulness-based stress reductionAnxietyDepression (economics)MedicineHeart failureMindfulnessPhysical therapyHappinessRating scaleInternal medicinePsychologyClinical psychologyPsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

Objective To study the effects of mindfulness-based stress reduction (MBSR) training on the negative emotions in elderly patients with chronic heart failure. Methods A total of 148 cases of elderly patients with chronic heart failure undergoing standard medication therapy for heart failure from February 2015 to February 2016 in the Internal Medicine-Cardiovascular Department of the First Affiliated Hospital of Zhengzhou University were recruited and divided in to experimental group (n=72) and control group (n=76) randomly. The patients of the control group were given health education, which on the basis of health education, the patients of the experimental group received MBSR training. After eight weeks of training, the Self-rating Anxiety Scale (SAS), Self-rating Depression Scale (SDS), and Memorial University of Newfoundland Scale of Happiness (MUNSH) were applied for patients' anxiety, depression and happiness in the two groups. Results After MBSR training, the scores of SAS, SDS and the negative score of MUNSH decreased in the experimental group (t=9.07, 2.59, 1.87; P<0.05). The positive and total score of MUNSH increased in the experimental group (t=4.19, 3.49; P<0.05). Conclusions MBSR training can improve anxiety, depression and subjective happiness in elderly patients with heart failure. Key words: Anxiety; Depression; Chronic heart failure; Mindfulness-based stress reduction; Subjective happiness

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score0.589

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 head0.008
GPT teacher head0.252
Teacher spread0.244 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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