Effectiveness of Abdominal Massage with Effleurage Technique for Constipation
Bibliographic record
Abstract
Constipation is a symptom, not a disease, the causes are improper diet, reduced fluid intake, lack of exercise, and certain medications. Constipation is characterized by defecating less than three times a week, and feces that are hard, dry, and difficult to expel, causing discomfort. One non-pharmacological therapy that can be used to treat constipation is abdominal massage with effleurage technique. The aim of this research was to analyze the effectiveness of abdominal massage using the effleurage technique against constipation in Dukuh Setro Village, Tambaksari District, Surabaya City. This research design uses a pre-experimental group pre-post test design, the research instrument uses an observation sheet, with a total of 31 respondents. The sampling technique used is simple random sampling. Data analysis used the McNemar statistical test. The results of the research before the abdominal massage were carried out with the effleurage technique all respondents (100%) experienced constipation after abdominal massage with the effleurage technique as many as 80.6% stated that they did not have constipation. The statistical test results obtained a significant p-value in the constipation category before and after abdominal massage with effleurage technique is p value = 0.000 with ɑ = < 0.05, this shows that abdominal massage with Effleurage technique is effective for treating constipation. Implications of abdominal massage research results with effleurage technique are One alternative action to treat constipation if done regularly and can become a new habit for adults who experience constipation.
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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 | 0.001 |
| 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) | 0.002 | 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".