Auricular Acupressure Combined with an Internet-Based Intervention or Alone for Primary Dysmenorrhea: A Control Study
Bibliographic record
Abstract
Background. Primary dysmenorrhea is prevalent in adolescents and young women. Menstrual pain and distress causes poor school performance and physiological damage. Auricular acupressure can be used to treat these symptoms, and Internet-based systems are a flexible way of communicating and delivering the relevant information. Objective. This study investigates the effects of auricular acupressure (AA) alone and combined with an interactive Internet-based (II) intervention for the management of menstrual pain and self-care of adolescents with primary dysmenorrhea. Design. This study adopts a pretest/posttest control research design with a convenience sample of 107 participants. Results. The outcomes were measured using the short-form McGill pain questionnaire (SF-MPQ), visual analogue scale (VAS), menstrual distress questionnaire (MDQ), and adolescent dysmenorrheic self-care scale (ADSCS). Significant differences were found in ADSCS scores between the groups, and in SF-MPQ, VAS, MDQ, and ADSCS scores for each group. Conclusion. Auricular acupressure alone and a combination of auricular acupressure and interactive Internet both reduced menstrual pain and distress for primary dysmenorrhea. Auricular acupressure combined with interactive Internet instruction is better than auricular acupuncture alone in improving self-care behaviors.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".