Prevalence of Dysmenorrhea and Determinants of Menstrual Distress in Adolescent Girls with Dysmenorrhoea, In Tirupati Town
Bibliographic record
Abstract
Background: Dysmenorrhoea is a common problem during menses in adolescent girls which affects their quality of life, academic activities, cannot attend social functions and use over the counter medicines which may lead to dangerous adverse effects and infertility. Objectives: 1. To study the prevalence of dysmenorrhea in adolescent girls studying in government municipal high schools of Tirupati. 2.To determine the significant factors related to menstrual distress in adolescent girls. Methodology: A cross-sectional study conducted among 320 adolescent girls studying in the selected government high schools of Tirupati, India. The study investigated symptoms, related factors and consequences of menstrual distress in adolescent girls with dysmenorrhea. Four instruments were used to collect data: Questionnaires on Demographic Data, Menstrual Distress Questionnaire, A Questionnaire related to Menstrual characters and A Short Form McGill Pin Questionnaire. Results: Prevalence of dysmenorrhoea among adolescent girls was found 67.7%. Regression analysis indicated that the best subset for predicting menstrual distress in adolescent girls included MPQ-SF, menstrual cycle in days, socioeconomic status and education. Conclusions: Majority (67.7%) of the adolescent girls were suffering with dysmenorrhoea. Menstrual distress is significantly correlated with impact on daily activities, absence from class, and analgesic usage.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".