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Record W3004473695 · doi:10.1002/bsl.2447

Sex and genes, part 2: A biopsychosocial approach to assess and treat challenging sexual behavior in persons with intellectual disabilities including fragile X syndrome and 22q11.2 deletion syndrome

2020· review· en· W3004473695 on OpenAlexaff
Nancy Miodrag, Deborah Richards, J. Paul Fedoroff, Shelley L. Watson

Bibliographic record

VenueBehavioral Sciences & the Law · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsUniversity of OttawaLaurentian UniversityMcMaster University
Fundersnot available
KeywordsBiopsychosocial modelPsychological interventionFragile X syndromeIntellectual disabilityClinical psychologyMedicineAffect (linguistics)Sexual dysfunctionPsychiatryPsychology

Abstract

fetched live from OpenAlex

Individuals with intellectual disabilities (IDs) - and specifically those with genetic disorders - are more prone to medical and psychological challenges that affect their sexual development, experiences, and fertility. In this review paper we first provide an overview of the biopsychosocial (BPS) model and then explain how the model can guide and improve the assessment and treatment of challenging sexual behaviors by persons with IDs. We discuss two genetic conditions - fragile X syndrome and 22q11.2 deletion syndrome - in case studies, showing how the BPS model can be used to assess and treat the sexual problems of individuals with various types of ID. We conclude with BPS-formulated treatment considerations in three key domains: biomedical treatment (e.g., medication side effects; stopping or changing medications), psychological treatment (e.g., providing psychological therapies), and socio-environmental interventions (e.g., providing socio-sexual education and staff training). Together, these treatment interventions can aid clinicians to prevent and/or treat problematic sexual behaviors of people with IDs.

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)0.000
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 score0.952
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.130
GPT teacher head0.340
Teacher spread0.210 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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