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Record W3015098074 · doi:10.5539/gjhs.v12n4p118

Self-Efficacy Training Improved The Quality of Life for Cancer Patients Undergoing Chemotherapy

2020· article· en· W3015098074 on OpenAlexvenueno aff
Agustina Boru Gultom, Indrawati Indrawati

Bibliographic record

VenueGlobal Journal of Health Science · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPancasila Values in Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuality of life (healthcare)CancerChemotherapyTest (biology)Analysis of varianceRepeated measures designPsychological interventionSelf-efficacyInternal medicineOncologyPhysical therapyNursingPsychologyPsychotherapist

Abstract

fetched live from OpenAlex

The survival of cancer patients is often short despite receiving therapy, but it really depends on various things including those that play an important role is the ability of patients to improve their life status in dealing with illnesses and therapy through increased self-efficacy because self-efficacy of cancer patients often experience reduction due to cancer and the therapy it undergoes. The pre–test and the first post -test and also the second post-test research design in the form of self-efficacy training of cancer patients undergoing chemotherapy used a quasi-experimental design without a control group. Data analysis used dependent t test and repeat ANOVA test. There were significant effects and differences in self-efficacy training on the quality of life of cancer patients undergoing chemotherapy with the p value dependent test was 0,000 and the repeat ANOVA test was p value 0,000. The uses of structured self-efficacy training be one of the considerations of nursing interventions to improve the quality of life of cancer patients undergoing chemotherapy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0050.000

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 head0.125
GPT teacher head0.469
Teacher spread0.344 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Journal of Health ScienceSame topicPancasila Values in EducationFrench-language works237,207