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Record W4281615936 · doi:10.1177/07334648221099728

Using the Intersectionality-Based Policy Analysis Framework to Evaluate a Policy Supporting Sexual Health and Intimacy in Long-Term Care, Assisted Living, Group Homes & Supported Housing

2022· article· en· W4281615936 on OpenAlexaffabout
Kate McBride, Marie Carlson, Bethan Everett

Bibliographic record

VenueJournal of Applied Gerontology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsVancouver Coastal HealthProvincial Health Services Authority
Fundersnot available
KeywordsIntersectionalityHuman sexualityEquity (law)Long-term careReflexivityReproductive healthHealth careHealth policySociologyPsychologyGerontologyPolitical scienceGender studiesNursingEconomic growthMedicineEconomicsSocial science

Abstract

fetched live from OpenAlex

Sexuality is an integral part of being human throughout life. This does not change when moving into long-term care (LTC). However, the sexual health of persons living in LTC is often overlooked. This paper presents an analysis of the recently released health organizational policy: Supporting Sexual Health and Intimacy in Long-Term Care, Assisted Living, Group Homes & Supported Housing. The Intersectionality-Based Policy Analysis Framework is used to outline the policy problem, examine how this policy was developed, and evaluate its potential to address the problem. Key findings are that both the development process and the policy constructs align with principles of intersectionality, such as equity, reflexivity, and diverse knowledges. In conclusion, this analysis suggests this policy is feasible, equitable and could effectively address sexual health for persons living in LTC, while leading to an improved workplace for staff. We recommend that this policy be more widely adopted across Canada.

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)0.068
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.073
Threshold uncertainty score0.399

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0680.052
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0070.006
Science and technology studies0.0050.008
Scholarly communication0.0120.008
Open science0.0020.007
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0060.000

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 head0.128
GPT teacher head0.474
Teacher spread0.346 · 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 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

Citations4
Published2022
Admission routes2
Has abstractyes

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