Premenstrual Syndrome in Thai Nurses
Bibliographic record
Abstract
OBJECTIVE: To investigate prevalence of premenstrual syndrome (PMS) and its associated factors among Thai nurses. METHODS: The study was conducted in 423 nurses in a university hospital during October 2005 to March 2006. Prevalence of PMS was determined using a validated Thai version of Premenstrual Symptoms Screening Tool (PSST). Factors associated with PMS were analyzed using Student t-test and Chi-square test. RESULTS: The prevalence of PMS in Thai nurses was 25.1%. Nurses with younger age, nulligravida, lower income, more coffee consumption, dysmenorrhea, and negative attitude toward menstruation had higher prevalence of PMS. After multiple logistic regression analysis, the significant factors associated with PMS were coffee consumption > 1 cups/day and negative attitude toward menstruation; odds ratios (95% confidence interval) were 2.322 (1.257 to 4.288) and 5.768 (2.096 to 15.872), respectively. CONCLUSION: According to the Thai PSST, 25.1% of Thai nurses are suffering from PMS. The significant associated factors were more coffee consumption and negative attitude toward menstruation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".