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Record W4402643059 · doi:10.11621/lpj-24-31

The Relationship of Psychological and Somatic Well-Being in Patients

2024· article· en· W4402643059 on OpenAlexaboutno aff
Yauhen G. Shvaiko

Bibliographic record

VenueMoscow University Psychology Bulletin · 2024
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSomatic cellPsychologyClinical psychologyBiologyGenetics

Abstract

fetched live from OpenAlex

Background. Understanding of emotional aspects of painful experiences can significantly expand the therapeutic tools for both doctors and psychologists. Objective. The focus is placed on the study of the relationship between psychological and somatic well-being of patients with pain syndrome. Study Participants. Sample included 119 people: 57 men (average age 47.9 years) and 62 women (average age 46.5 years) from the number of outpatients of the treatment and prevention institution “City Health Center #12” (Minsk). Methods. The study used: the Subjective Well-being Scale, the Numeric Pain Rating Scale (NPRS), the McGill Pain Questionnaire (MPQ), the Differential Emotion Scale, the Toronto Alexithymia Scale, and the Giessen Subjective Complaints List, GBB. Results. The analysis of the correlations of emotional state and somatic complaints has revealed similarities and specificity of subjective experiences of patients in different conditions. The correlation analysis showed a certain relationship between emotional state and well-being, which is closely related to subjective well-being. Conclusion. Subjective well-being of patients is related to psychoemotional state, health and somatic complaints, as well as, to some extent, to the level of alexithymia. Patients' well-being is related to their emotional state and negative emotions. Practical application of the results. Understanding the psychological state of the patient can be useful for developing a personalized approach to treatment and successful rehabilitation through intrapersonal resources.

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 score0.111
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.316
Teacher spread0.294 · 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
Published2024
Admission routes1
Has abstractyes

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