Menstrual characteristics and dysmenorrhea among Palestinian adolescent refugee camp dwellers in the West Bank and Jordan: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Women and girls experience menstruation throughout their reproductive years. Normal adolescent menstrual cycles gauge current and future reproductive health. Dysmenorrhea (painful menstruation) is the most prevalent menstrual disturbance in adolescents that can be debilitating. This study examines the menstrual characteristics of adolescent girls living in Palestinian refugee camps in the West Bank of the Israeli-occupied Palestinian territory and Jordan, including estimates of dysmenorrhea levels and associated factors. METHODS: A household survey of 15 to 18-year-old adolescent girls was conducted. Trained field workers collected data on general menstrual characteristics and dysmenorrhea level using Working ability, Location, Intensity, Days of pain Dysmenorrhea scale (WaLIDD), in addition to demographic, socio-economic, and health characteristics. The link between dysmenorrhea and other participant characteristics was assessed using a multiple linear regression model. Additionally, data on how adolescent girls cope with their menstrual pain was collected. RESULTS: 2737 girls participated in the study. Mean age was 16.8 ± 1.1 years. Mean age-at-menarche was 13.1 ± 1.2; mean bleeding duration was 5.3 ± 1.5 days, and mean cycle length was 28.1 ± 6.2 days. Around 6% of participating girls reported heavy menstrual bleeding. High dysmenorrhea levels were reported (96%), with 41% reporting severe symptoms. Higher dysmenorrhea levels were associated with older age, earlier age-at-menarche, longer bleeding durations, heavier menstrual flow, skipping breakfast regularly, and limited physical activity patterns. Eighty nine percent used non-pharmacological approaches to ease menstrual pain and 25% used medications. CONCLUSION: The study indicates regular menstrual patterns in terms of length, duration, and intensity of bleeding and a slightly higher age-at-menarche than the global average. However, an alarmingly high prevalence of dysmenorrhea among participants was found that tends to vary with different population characteristics, some of which are modifiable and can be targeted for better menstrual health.This research emphasizes the need for integrated efforts to assist adolescents with menstrual challenges such as dysmenorrhea and irregular periods to achieve informed recommendations and effective actions.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".