Affirmation-Tapping to Reduce Pain Perception and Glutamate Serum Levels of Post-cesarean Patients
Bibliographic record
Abstract
Introduction: Affirmation, by praying and positive evaluation accompanied by acupoint stimulation, has been shown to reduce pain in postoperative patients. In other studies, affirmations can reduce chronic pain due to the down-regulation performance of Glutamate receptors. Acupuncture can reduce pain complaints by modulating Glutamate at the spinal level. So it is suspected that affirmation-tapping can reduce postsurgical pain due to modulation of Glutamate; however clinical studies have not been conducted. The aim is to compare the pain perception of postoperative patients given affirmation-tapping therapy with other treatment patients as complementary nursing interventions. This is to see if the modulation of the performance of serum Glutamate levels is different from other treatments. Methods: We used a randomized post-test control group design that was performed parallel in post-cesarean patients. A sample of 40 patients was divided into four groups (10 in affirmations, 10 in tapping, 10 in affirmation-tapping and 10 in controls). They were obtained through simple random sampling. The instruments included affirmation-tapping guides, Elisa kits and McGill Melzack Pain's short questionnaire form (MPQ-sf). The independent variable was the affirmation-tapping intervention and the dependent variable was the perception of pain and Glutamate serum level. Data were analyzed using simple linear regression. Results: The average of Glutamate levels in the Affirmation-tapping treatment group was lower (0. 0340. 004) pg/mL and significantly different (Sig=0.00) from other groups (0. 0560. 011) pg/mL. Conclusion: Affirmation-tapping as a complementary nursing intervention decreases pain perception and Glutamate serum levels in patients post-cesarean section that supports conventional treatment. Affirmation-tapping is recommended as an intervention to overcome pain perception in postoperative nursing patients who support conventional treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".