HOW MUCH DO CANADIAN SOCIAL WORKERS KNOW ABOUT PREMENSTRUAL SYNDROME AND PREMENSTRUAL DYSPHORIC DISORDER, AND DOES THIS AFFECT THEIR ASSESSMENT OF MOTHERS?
Bibliographic record
Abstract
PMS and PMDD symptoms interfere in some women’s daily coping abilities, including their mothering. Social workers assess mothering ability but may not understand the negative effects of PMS/PMDD. This study examines social workers’ knowledge about PMS/PMDD and whether this influences their assessments with mothers, surveying 521 Canadian social workers. The Premenstrual Experience Knowledge Questionnaire (PEKQ) assesses the biopsychosocial aspects of premenstrual knowledge. Social workers scored an average of 60.5%. They were least knowledgeable about SSRI treatments, suicide rates, and symptoms. Higher scores were associated with having one’s own premenstrual symptoms and PMS symptoms that interfered more in one’s life. Only 5.1% of social workers addressed PMS/PMDD in their mothering assessments, with significant relationships between PMS/PMDD inquiry and worker age, knowledge scores, training, and personal premenstrual symptoms. These results can educate social workers, raising awareness of the possible negative impacts of PMS/PMDD on mothering, which could lead to changing their assessment practices and identifying these treatable conditions. This awareness-raising is especially critical when PMDD/PMS affects mothering to the degree that children’s safety might be compromised.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".