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Record W3177207329 · doi:10.3390/geriatrics6020060

LGBTQ+ Aging Research in Canada: A 30-Year Scoping Review of the Literature

2021· article· en· W3177207329 on OpenAlexafffundabout
Kimberley Wilson, Arne Stinchcombe, Sophie M. Regalado

Bibliographic record

VenueGeriatrics · 2021
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsNOSM UniversityBrock UniversityUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTransgenderLesbianQueerGerontologyStigma (botany)Inclusion (mineral)MedicineScope (computer science)Relevance (law)Social stigmaQuality of life (healthcare)Lived experienceGender studiesHuman immunodeficiency virus (HIV)PsychologyPsychiatryNursingSociologyFamily medicinePsychotherapistPolitical science

Abstract

fetched live from OpenAlex

Canada has a unique socio-political history concerning the inclusion of lesbian, gay, bisexual, transgender, and queer (LGBTQ+) people. With aging populations, understanding diverse groups of older adults is paramount. We completed a systematic search and scoping review of research in Canada to quantify and articulate the scale and scope of research on LGBTQ+ aging. Our search identified over 4000 results and, after screening for relevance, our review focused on 70 articles. Five major themes in the literature on LGBTQ+ aging in Canada were identified: (1) risk, (2) HIV, (3) stigma, and discrimination as barriers to care, (4) navigating care and identity, (5) documenting the history and changing policy landscapes. Most of the articles were not focused on the aging, yet the findings are relevant when considering the lived experiences of current older adults within LGBTQ+ communities. Advancing the evidence on LGBTQ+ aging involves improving the quality of life and aging experiences for LGBTQ+ older adults through research.

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)0.021
metaresearch head score (Gemma)0.071
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score0.564

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.071
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0330.064
Science and technology studies0.0050.003
Scholarly communication0.0080.003
Open science0.0030.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.080
GPT teacher head0.437
Teacher spread0.357 · 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.

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

Citations9
Published2021
Admission routes3
Has abstractyes

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