The hidden burden of dysmenorrhea among adolescent girls in Palestine refugee camps: a focus on well-being and academic performance
Bibliographic record
Abstract
BACKGROUND: Dysmenorrhea (painful menstruation) is a condition that may have a profound effect on adolescent girls' health status and well-being. It can impede their engagement in daily activities and hamper their regular school attendance. This study aims to explore the relationship between dysmenorrhea, well-being, and academic performance among adolescent girls living in Palestine refugee camps in the West Bank and Jordan. METHODS: We conducted a household survey between June and September 2019, with a total sample of 2737 adolescent girls 15 to 18 years old. Dysmenorrhea severity was assessed using the Working Ability, Location, Intensity, Duration of pain Dysmenorrhea scale (WaLIDD). The WHO-5 scale was used to evaluate the girls' overall well-being. Menstrual academic disruption (MAD) was measured using a self-reported scale. Multiple linear regression models were employed to evaluate the association between dysmenorrhea, well-being, and academic performance. Directed Acyclic Graphs (DAGs) were employed to identify variables for control in regression models. RESULTS: The mean dysmenorrhea score was 6.6 ± 2.6, with 37.9% and 41.2% expressing moderate and severe symptoms, respectively. The mean WHO-5 score was 58.7 ± 25.1, and 34.9% reported a low well-being status. The mean MAD score was 3.1 ± 3.3. 26% reported missing school due to dysmenorrhea, 36% said dysmenorrhea impacted their ability to concentrate, and 39% were unable to study for tests, and complete homework. The first regression analysis showed a reduction of 1.45 units in WHO-5 score for each unit increase in dysmenorrhea. The second regression analysis showed a non-linear increase in MAD score for increasing dysmenorrhea. For each dysmenorrhea score less than 4 (mild) there was a modest increase in MAD scores (coefficient 0.08, p-value = 0.006), and for each dysmenorrhea score above 4 there was a stronger increase in MAD scores (coefficient 0.95, p < 0.001). CONCLUSION: Dysmenorrhea poses significant challenges to the well-being and academic performance of adolescent girls living in Palestine refugee camps. Collaborative efforts and multifaceted approaches are crucial to address dysmenorrhea effectively. This involves research, targeted interventions, culturally sensitive strategies, and fostering a supportive environment that empowers girls to thrive academically and beyond.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".