Perception and Healthcare seeking practices regarding dysmenorrhea among public secondary school students in Lagos, Nigeria
Bibliographic record
Abstract
Objectives: Dysmenorrhea is an important health problem which may have a negative impact on female health, school activities and psychological status. This study assessed the prevalence, knowledge, perception and healthcare seeking practices of dysmenorrhea among secondary school students in Lagos State, Nigeria. The pattern of management of dysmenorrhea among respondents was also assessed. Methods: A cross-sectional descriptive survey was conducted among 420 adolescents in public secondary schools in Ikeja Local government area of Lagos State, Nigeria. Respondents were interviewed using a structured pretested questionnaire. Data was analyzed with SPSS Version 22.0. The level of statistical significance was set at p≤0.05. Results: Mean ± SD age at menarche was 12.3±1.3 years. The prevalence of dysmenorrhea among respondents was 75.2%. About one-quarter 106(25.2%) had good knowledge and 209(49.8%) had good perception of dysmenorrhea. Only 10% had ever sought health care for dysmenorrhea. A statistically significant association was found between the knowledge of respondents and healthcare-seeking behavior towards dysmenorrhea (p = 0.004). Conclusion: The prevalence of dysmenorrhea was high and majority of respondents had poor knowledge. Improving adolescents’ knowledge of dysmenorrhea through health education could positively influence their health care-seeking behavior.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".