The Application of Spiritual Emotional Freedom Technique on Pain in Cancer Patients
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".