Investigation of Premenstrual Syndrome among the Students of Medical Sciences
Bibliographic record
Abstract
INTRODUCTION: Premenstrual syndrome (PMS) is the advent of physical and psychological symptoms related to the menstrual cycle, the symptoms of this syndrome start in luteal phase and ends at the end of menstrual period. During the last decades, the patterns of PMS (PMS) have studied in a wide range. But those researches had had different methodologies and definitions and the results were not well comparable. Hence, the researchers decided to conduct a study with the aim of investigation of the prevalent of PMS among the students of the Zahedan University of Medical Sciences. MATERIALS & METHODS: This descriptive–analytical study was done on 200 students of Zahedan University of Medical Sciences, Iran. A two-part questionnaire was used in order to collect data. The first part related to the demographic features and the second part was related to the PSTT standard questionnaire. After collecting data, the data was analyzed by using SPSS 19 software through the statistical descriptive tests, Chi square test, Fisher’s exact test and t-test. FINDINGS: The mean age of subjects was 21.9 ± 2.61. A total of 89 subjects were diagnosed with PMS. The most percentage of moderate to severe PMS was for students of medicine and the least percentage was for students of nursing. The highest percentage of mild PMS was in nursing students while the lowest percentage was for students of medicine. CONCLUSION: Regarded to the fact that PMS is from the common problems of premenopausal ages in women and a high percentage of them are with psychological and physical symptoms, and since this condition can cause adverse effects on the quality of women’s life; hence, it is necessary to consider the supportive and therapeutic strategies in order to reduce the severity of its symptoms and adverse effects.
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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 | 0.001 | 0.002 |
| 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".