The Premenstrual Assessment Form: Short Form (PAF-SF) – Additional Psychometric Analyses of a Brief Measure of Premenstrual Symptoms
Bibliographic record
Abstract
The Premenstrual Assessment Form–Short Form (PAF-SF) is a 10-item measure that assesses premenstrual symptom severity. There is little research assessing the PAF-SF’s psychometrics and proposed subscales (affect/water retention/pain). This chapter aims to assess the 10-item PAF-SF’s psychometric properties (i.e., internal consistency, and structural/criterion-related/known groups validity). Eighty-seven naturally cycling females (Mage = 28.86 years, SD = 6.11) participated. Participants completed the 10-item PAF-SF; the State-Trait Anxiety Inventory–Trait subscale (STAI-T); and the Structured Clinical Interview for DSM-5 (SCID-5) premenstrual dysphoric disorder (PMDD) module. With principal components analysis, we extracted and compared three-factor (affect/water retention/pain) and two-factor (psychological/physiological) solutions for the PAF-SF. The two-factor solution was selected for its greater interpretability, simple structure, internal consistencies, and parsimony. Participants with versus without a provisional PMDD diagnosis had higher psychological subscale scores; unexpectedly, PMDD group differences were not observed on the physiological subscale. Psychological, but not physiological, subscale scores were positively correlated with trait anxiety and PMDD affective symptom count. Scores on the physiological subscale were positively correlated with the PMDD somatic symptom count. Psychological subscale scores were also positively correlated with the PMDD somatic symptom count. The 10-item PAF-SF appears to be a reliable and valid measure of premenstrual symptom severity and comprises psychological and physiological symptom domains.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".