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Record W4389885508 · doi:10.1177/07334648231219414

“I Want to Grow Older With Dignity”: Older LGBTQ+ Canadian Adults’ Perceptions and Experiences of Aging

2023· article· en· W4389885508 on OpenAlexafffundabout
Laura Hurd Clarke, Lynda Y. K. Li

Bibliographic record

VenueJournal of Applied Gerontology · 2023
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsThematic analysisGerontologyAutonomyPopulation ageingInclusion (mineral)PerceptionPsychologyQualitative researchSuccessful agingPopulationSociologyMedicineSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

The number of older LGBTQ+ adults is growing worldwide. Yet few studies outside of the United States have examined their experiences of aging. Drawing on the Health Equity Promotion Model and contextualized in Canada's unique socio-political history, our study used multiple, in-depth, qualitative interviews to examine 30 older Canadian LGBTQ+ adults' (aged 65-83) perceptions and experiences of growing older. Our descriptive thematic analysis identified three overarching categories: "Losses," "gains," and "needs." Losses referred to the changes in the participants' health, autonomy, and relationships that had occurred with age. Gains entailed positive later life changes, including increased wisdom, flexibility, and social connections. Finally, needs referred to those things that the participants deemed essential for aging well, namely, inclusive health care, meaningful activities, and supportive networks. We discuss the policy and practice implications of our findings for the fostering of health, well-being, and social inclusion amongst this often-marginalized population.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.338
Teacher spread0.312 · 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.

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

Citations10
Published2023
Admission routes3
Has abstractyes

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