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Record W2889359753 · doi:10.22037/nbm.v6i2.21235

Assessing the Effectiveness of Cognitive Behavioral Stress Management (CBSM) on Anxiety and Depression of Cancer Patients

2018· article· en· W2889359753 on OpenAlexaboutno aff
Ali Asadbeygi, Hasan Ahadi, Hamid Reza Mirzai

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer-related cognitive impairment studies
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietyDepression (economics)CognitionPsychologyStress managementClinical psychologyCancerStress (linguistics)PsychotherapistMedicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

<h2>Background: The purpose of this study was to determine the effectiveness of cognitive-behavioral stress therapy on stress, depression and distress in patients with cancer.</h2> <strong>Cases Report</strong><strong>: </strong>In a cross-sectional study of consecutive patients (Aged 32-70 years, progression of their disease was at levels 1 to 3, high cycle education, and 3 months of chemotherapy, of which 40, were randomly available from this group (20 experimental and 20 Control group). The instrument was a McGill Pain Questionnaire (1997) and the Hazards and Anxiety and Depression Scale (HADS) questionnaire. Data were analyzed using two methods of Kolmogorov-Smirnov inferential statistics and multivariate analysis of covariance using software software Spss17. <strong>C</strong><strong>onclusion: </strong>Correlation analysis showed that the experimental group had a significant reduction in depression and anxiety in the posttest after the control group compared with the control group. The short-term cognitive-behavioral stress management program can reduce, depression and anxiety in cancer patients.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.164
GPT teacher head0.570
Teacher spread0.406 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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