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Record W2942522525 · doi:10.1097/sga.0000000000000390

Attitudes About Coping With Fatigue in Patients With Gastric Cancer

2019· article· en· W2942522525 on OpenAlexaff
Eun Ja Yeun, Misoon Jeon

Bibliographic record

VenueGastroenterology Nursing · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsEmergent BioSolutions (Canada)
Fundersnot available
KeywordsCoping (psychology)MedicineCancer-related fatiguePsychological interventionClinical psychologyCancerPhysical therapyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Cancer-related fatigue is the most common symptom in patients with cancer. Coping methods for cancer-related fatigue differ from those of patients without cancer, as the situations faced by patients with cancer are unique. This study aimed to identify subjectivity concerning coping with fatigue in Korean patients with gastric cancer. Q-methodology was used to examine subjective perceptions regarding coping with fatigue among Korean patients with gastric cancer. A convenience sample of 33 participants, who had been hospitalized in 2 university hospitals in South Korea, was recruited to participate in the study and 37 selected Q-samples were classified into a normal forced distribution using a 9-point bipolar grid. The obtained data were analyzed by using PC-QUANL for Windows. Three factors representing distinct attitudes about coping with fatigue emerged among Korean patients with gastric cancer: an optimistic mind, dependency on medicine, and exercise preference. The 3 factors explained 39.4% of the total variance (23.7%, 7.9%, and 7.8%, respectively). Based on the study findings, it is important to develop customized nursing interventions that consider the characteristics of each patient group with gastric cancer. Health professionals should assess the attitudes of patients with gastric cancer about coping with fatigue, explore their situation, and consider their lifestyle.

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 score0.003
Threshold uncertainty score0.518

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 head0.011
GPT teacher head0.270
Teacher spread0.260 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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