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Record W4413440541 · doi:10.1111/jir.70010

Knowledge and Understanding of Menstrual Health in Women With Intellectual Disabilities: A Brief Report

2025· article· en· W4413440541 on OpenAlexafffund
Laura St. John, A.G. Robertson, Patricia K. Doyle–Baker, Yona Lunsky

Bibliographic record

VenueJournal of Intellectual Disability Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsUniversity of TorontoUniversity of CalgaryCentre for Addiction and Mental Health
FundersUniversity of Calgary
KeywordsIntellectual disabilityPsychologyDevelopmental psychologyPsychiatryMedicineClinical psychologyGerontology

Abstract

fetched live from OpenAlex

BACKGROUND: Women with intellectual disabilities (ID) often lack effective education and support surrounding menstrual health. This can directly impact self-care, participation and regular engagement in sport and more generally, health and wellbeing. METHODS: Twenty-two athletes from the Special Olympics were surveyed to assess knowledge and understanding of menstrual health. Responses were analyzed descriptively to identify their gaps in knowledge and understanding. RESULTS: Many participants had misconceptions about menstrual hygiene, with most incorrectly estimating how frequently sanitary products should be changed. Few participants used tampons, which likely impacted their menstrual management, especially during sport activities. Additionally, knowledge about menopause was notably low, with no significant difference between age groups. CONCLUSIONS: This study highlights a critical need for targeted education and resources to improve menstrual health knowledge among women Special Olympic athletes with ID. Addressing these gaps can enhance their independence, quality of life and sport participation. Further research and interventions are necessary to better support this population in managing their menstrual health effectively.

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)0.013
metaresearch head score (Gemma)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.008
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.151
GPT teacher head0.462
Teacher spread0.311 · 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 designQualitative
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
Published2025
Admission routes2
Has abstractyes

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