Prevalence of dysmenorrhea and associated factors among students in Ethiopia: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Dysmenorrhea is one of the most common gynecological complaints among adolescent women. It has been associated with short-term absenteeism in school and has a negative impact on academic and daily activities. Therefore, the aim of the study was to show the evidence on the magnitude and correlates of dysmenorrhea in Ethiopia. Method: In this systematic review and meta-analysis, we searched the literature from different databases such as PubMed/Medline, Science Direct, PsycINFO, and Cochrane library. We also used unpublished literature from Google, Google Scholar. The quality of the included articles was assessed using the Newcastle-Ottawa Scale. Data were extracted using a Microsoft Excel data extraction format. STATA version 14 statistical software was used for data analysis. To assess the heterogeneity of the primary articles, the Cochrane Q test statistics and the I 2 test were carried out. Publication bias was inspected by funnel plot, and Egger’s test was performed to confirm the presence of publication bias. A random-effects meta-analysis was used to estimate the pooled prevalence of dysmenorrhea and its associated factors. Result: A total of 12 studies were included in the final meta-analysis. The pooled prevalence estimate of dysmenorrhea among female students in Ethiopia is 71.69% (66.82%–76.56%). In our systematic review, among factors associated with dysmenorrhea, the family history of dysmenorrhea was frequently reported in included studies. Therefore, dysmenorrhea was significantly associated with a family history of dysmenorrhea (adjusted odds ratio = 4.69 (95% confidence interval: 2.80–7.85)). Conclusion: The pooled prevalence estimate of dysmenorrhea among students was much higher in Ethiopia. Health professionals and teachers should educate and support students to follow their menstrual cycle regularly in the event of irregular periods. There should be an awareness of the negative consequences of dysmenorrhea to reduce the physical and psychological stresses that affect women and their families.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".