Menstrual Pain Intensity, Coping, and Disability: The Role of Pain Catastrophizing
Bibliographic record
Abstract
OBJECTIVE: Menstrual pain or primary dysmenorrhea has not received much attention in the field of pain research. Little is understood about the effects menstrual pain has on the women who experience it. No studies to date have examined the cognitive factors related to the perceived intensity and coping of menstrual pain. To investigate these areas further, this study examined the associations between pain catastrophizing and how women perceive and cope with menstrual pain. DESIGN: A prospective and retrospective between-subjects study. PARTICIPANTS: Ninety-three undergraduate women, with a regular menstrual period and no preexisting pain disorder (e.g., endometriosis) that affects menstrual pain, were classified into high or low pain catastrophizing groups. OUTCOME MEASURES: Participants completed several self-reported questionnaires assessing pain catastrophizing, menstrual pain intensity, coping, and disability. RESULTS: High pain catastrophizers, in comparison with low pain catastrophizers, reported greater menstrual pain intensities, greater affective menstrual pain intensity, greater variability in the use of pain coping strategies, lower perceived effectiveness of over-the-counter medications and nonmedical pain coping strategies, and greater disability. CONCLUSIONS: The results extend our knowledge about the associations between pain catastrophizing and menstrual pain, reemphasize that pain experience is best viewed as a multidimensional construct, and have implications for the management of menstrual pain.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".