Primary Dysmenorrhea and Menstrual Symptoms in Indian Female Students: Prevalence, Impact and Management
Bibliographic record
Abstract
BACKGROUND: Dysmenorrhea is the most common gynecological problem among females and it is defined as cramping pain in the lower abdomen occurring just before or during menstruation. Menstrual symptoms are a broad collection of affective and somatic concerns that occur around the time of menses. The effect and importance of dysmenorrhea is very wide, therefore managing the problem is important. OBJECTIVE: To ascertain the prevalence, and impact of primary dysmenorrhea in student girls and their management behaviors. METHODS & MATERIALS: A Cross-sectional study was conducted on 1000 healthy females aged 11-28 years. Standardized Self-reporting questionnaires were used to obtain relevant data. Pain intensity was assessed by using the Numerical Pain Scale (NPS). Data was analyzed by SPSS version 16. RESULTS: Prevalence of dysmenorrhea was 70.2%. Majority of the subjects experienced pain for one or 1-2 days during menstruation. 23.2% of the dysmenorrheic girls experienced pain for 2-3 days. The most common symptom in both dysmenorrheic and non dysmenorrheic girls during the menstrual periods was tiredness and second most prevalent symptom was back pain.Females experiencing mild pain on an average absented for one and half day a month while 2.1±1.2 and 2.5±1.3 days for those who experienced moderate and severe forms of dysmenorrhea respectively. A small proportion of girls sought pharmacological management (25.5%) and 83.2% depended on non-pharmacological methods. Only 14.2% had sought medical advice. CONCLUSION: Sub optimal use of the medical advice and the barriers to seek medical attention by dysmenorrheic females need exploration. It is important that health education on puberty and menstruation is regarded as inadequate for many girls in India.
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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) | 0.002 | 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".