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Record W4389534439

The Effectiveness of Self-Compassion Training and Positive Thinking on Sleep Quality and Decrease of Pain Intensity in Girl With Primary Dysmenorrhea

2020· article· en· W4389534439 on OpenAlexaboutno aff
Karameh Saghebi Saeedi, Abbas Abolghasem, Bahman Akbari

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2020
Typearticle
Languageen
FieldMedicine
TopicMenstrual Health and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsGirlCompassionSleep qualityIntensity (physics)PsychologyPhysical therapyMedicineClinical psychologyDevelopmental psychologyPsychiatryCognition
DOInot available

Abstract

fetched live from OpenAlex

Objective: the low sleep quality and pain in girls with primary dysmenorrhea, this study aimed to investigate the effectiveness of self-compassion training and positive attitude to experience sleep quality and decrease of pain intensity in girls with primary dysmenorrhea. Methods: The research was semi-experienced that was conducted as a multi-group, pre-test, and post-test. The sample of the study was girls with primary dysmenorrhea, which were characterized among students in the first district of Rasht City (first and second high school) and were placed in three experimental and control groups. To collect the data and to screen the questionnaire for premenstrual symptoms, Peters Burg’s sleep quality questionnaire, and McGill’s intensity of pain questionnaire were applied. A one-way Analysis Of Covariance (ANCOVA) was used for analyzing data. Results: Positive thinking training and self-compassion training were effective in increasing sleep quality and decreased intensity of pain in girls with primary dysmenorrhea (P<0.001). The effect of positive thinking on quality of life was more than compassion training (P<0.005), but self-compassion does not affect pain severity. Conclusion: Findings indicate the impact of training and self-compassion in preventing the neuropsychological well-being of girls with primary dysmenorrhea.

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)0.006
metaresearch head score (Gemma)0.001
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 score0.049
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
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 head0.183
GPT teacher head0.511
Teacher spread0.327 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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Same venueDOAJ (DOAJ: Directory of Open Access Journals)Same topicMenstrual Health and DisordersFrench-language works237,207