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Record W2479372657 · doi:10.1093/arclin/acw046

Sexual Consent Capacity Assessment with Older Adults

2016· review· en· W2479372657 on OpenAlexaff
Maggie L. Syme, Debora Steele

Bibliographic record

VenueArchives of Clinical Neuropsychology · 2016
Typereview
Languageen
FieldMedicine
TopicPatient Dignity and Privacy
Canadian institutionsLeeds, Grenville & Lanark District Health Unit
Fundersnot available
KeywordsHuman sexualityPsychologyInformed consentCognitionHealth careGerontologyMedicinePsychiatryAlternative medicine

Abstract

fetched live from OpenAlex

Many healthcare providers have a limited knowledge of sexual and intimate expression in later life, often due to attitudinal and informational limitations. Further, the likelihood of an older adult experiencing cognitive decline increases in a long-term care (LTC) setting, complicating the ability of the providers to know if the older adult can make his or her own sexual decisions, or has sexual consent capacity. Thus, the team is left to question if and how to support intimacy and/or sexuality among residents with intimacy needs. Psychologists working with LTC need to be aware and knowledgeable about sexual consent capacity in older adulthood to be prepared to conduct evaluations and participate in planning care. Limited research is available to consult for best practices in sexual consent capacity assessment; however, models of assessment have been developed based on the best available evidence, clinical judgment, and practice. Existing models will be discussed and an integrated model will be illustrated via a case study.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.258
GPT teacher head0.506
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations16
Published2016
Admission routes1
Has abstractyes

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