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Record W4391644030 · doi:10.7202/1108986ar

HOW MUCH DO CANADIAN SOCIAL WORKERS KNOW ABOUT PREMENSTRUAL SYNDROME AND PREMENSTRUAL DYSPHORIC DISORDER, AND DOES THIS AFFECT THEIR ASSESSMENT OF MOTHERS?

2024· article· en· W4391644030 on OpenAlexvenueaboutno aff
Lynn Barry, Leslie M. Tutty

Bibliographic record

VenueCanadian social work review · 2024
Typearticle
Languageen
FieldMedicine
TopicMenstrual Health and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsPremenstrual dysphoric disorderPsychologyAffect (linguistics)Biopsychosocial modelClinical psychologyPsychiatryCoping (psychology)Developmental psychologyMedicineMenstrual cycle

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.305
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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