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Record W3128323487 · doi:10.37287/ijghr.v2i4.248

The Application of Spiritual Emotional Freedom Technique on Pain in Cancer Patients

2019· article· en· W3128323487 on OpenAlexaboutno aff
Niken Sukesi, Wahyuningsih Wahyuningsih, Heny Prasetyorini

Bibliographic record

VenueIndonesian Journal of Global Health Research · 2019
Typearticle
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical therapyRating scaleDescriptive statisticsCancerQuality of life (healthcare)Cancer painPsychologyMcGill Pain QuestionnaireMedicineClinical psychologyPsychotherapistVisual analogue scaleDevelopmental psychologyInternal medicineStatistics

Abstract

fetched live from OpenAlex

Cancer patients often experience pain complaints on their parts of body. The pain felt by patients can interfere with the patient's daily activities, leading to a decreased quality of life. The provision of Spiritual Emotional Freedom Technique (SEFT) to cancer patients is done to help overcome the problem of physical and psychic pain. This therapy combines body energy and spiritual therapy using three stages consisting of the setup, the tune in, and the tapping. A mild tapping or tapping method is given at 18 points on the body. This study aimed to find out the extent of the effect of SEFT on the pain of cancer patients. This research was a descriptive through a case study approach. The number of participants was 4 participants. The sampling was done purposively. The inclusion criteria of this study are cancer patients who complained moderate to severe pain. The pain level was measured using the Numeric Rating Scale (NRS). The data were collected using literature studies, in-depth interviews, and observations.  Data analysis was done by using an interactive model that classifies the process into data reduction, data presentation, and conclusion drawing (Verification). The application of case studies using SEFT theory has a meaningful influence to reduce pain.  

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)0.022
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 score0.312
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.062
GPT teacher head0.427
Teacher spread0.365 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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