MétaCan
Menu
Back to cohort
Record W4200268514 · doi:10.1177/10497323211050373

“You Could Tell I Said the Wrong Things”: Constructions of Sexual Identity Among Older Gay Men in Healthcare Settings

2021· article· en· W4200268514 on OpenAlexafffund
Hannah Kia, Travis Salway, Ashley Lacombe‐Duncan, Olivier Ferlatte, Lori E. Ross

Bibliographic record

VenueQualitative Health Research · 2021
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversity of TorontoUniversité de MontréalSimon Fraser UniversityPublic Health OntarioUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsPsychologyIdentity (music)OppressionHealth careSexual identitySexual orientationConstruct (python library)Social psychologyGender studiesHuman sexualitySociologyPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Older gay men commonly conceal their sexual identity in healthcare settings due to past experiences and expectations of encountering stigma and discrimination in these contexts. Although insights on how older gay men construct their sexual identity in healthcare may help contextualize this phenomenon, this question remains under-explored. Accordingly, we present the findings of a secondary grounded theory analysis of individual interview data, which we originally collected to examine the healthcare experiences of 27 gay men ages 50 and over, to explore constructions of sexual identity among the group. Our findings broadly reveal that older gay men's varying exposure to intersecting systems of oppression, together with their perceptions of different healthcare settings, may be critical in shaping their constructions of sexual identity in these contexts. Our research supports the need for healthcare policies and practices that address stigma and discrimination as salient barriers to sexual identity disclosure among older gay men.

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)0.021
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.003
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 head0.329
GPT teacher head0.611
Teacher spread0.283 · 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 designObservational
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

Citations16
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueQualitative Health ResearchSame topicLGBTQ Health, Identity, and PolicyFrench-language works237,207