Validation of the Brazilian Portuguese version of the Premenstrual Symptoms Screening Tool (PSST) and association of PSST scores with health-related quality of life
Bibliographic record
Abstract
OBJECTIVE:: To develop and validate a Brazilian Portuguese version of the Premenstrual Symptoms Screening Tool (PSST), a questionnaire used for the screening of premenstrual syndrome (PMS) and of the most severe form of PMS, premenstrual dysphoric disorder (PMDD). The PSST also rates the impact of premenstrual symptoms on daily activities. METHODS:: A consecutive sample of 801 women aged ≥ 18 years completed the study protocol. The internal consistency, test-retest reliability, and content validity of the Brazilian PSST were determined. The independent association of a positive screen for PMS or PMDD and quality of life determined by the World Health Organization Quality of Life instrument-Abbreviated version (WHOQOL-Bref) was also assessed. RESULTS:: Of 801 participants, 132 (16.5%) had a positive screening for PMDD. The Brazilian PSST had adequate internal consistency (Cronbach's alpha = 0.91) and test-retest reliability. The PSST also had adequate convergent/discriminant validity, without redundancy. Content validity ratio and content validity index were 0.61 and 0.94 respectively. Finally, a positive screen for PMS/PMDD was associated with worse WHOQOL-Bref scores. CONCLUSIONS:: These findings suggest that PSST is a reliable and valid instrument to screen for PMS/PMDD in Brazilian women.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".