Dysmenorrhea and the clinical encounter: testing a conceptual model of physician–patient interactions among emerging adults
Bibliographic record
Abstract
ABSTRACT: Dysmenorrhea affects as much as 85% of female youth in Canada and the United States and can negatively impact academic performance, overall health, and mental well-being. The physician-patient relationship can play an important role in supporting patients with pain conditions, such as dysmenorrhea. Through effective communication, trust, and validation, physician-patient interactions can empower pain patients, potentially improving pain outcomes. To date, no studies have quantitatively examined the impact of physician-patient interactions on youth's experiences of dysmenorrhea. Therefore, our aim was to explore the relationships among perceived physician communication, pain invalidation, trust in the physician, treatment adherence, menstrual sensitivity, and dysmenorrhea symptom severity among emerging adults (EA) and test a conceptual model of potential interactions using partial least squares structural equation modeling (PLS-SEM). The online survey was administered to Canadian and American EA aged 18 to 21 ( Mage = 19.4, SD = 1.1) years with dysmenorrhea. Two models were tested using PLS-SEM: model A only included participants who had received a treatment plan from their physician (n = 279) and model B included the full data set (N = 362). In both models, the perception of more effective physician communication and reduced pain invalidation were related to lower dysmenorrhea symptom severity through menstrual sensitivity. In model A, better physician communication and lower pain invalidation were also associated with higher reported treatment adherence by trust in the physician; however, neither treatment adherence nor trust in the physician were associated with dysmenorrhea symptom severity. Future research should include additional elements within the clinical encounter and further refine the model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".