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Record W4384568705 · doi:10.36315/2023inpact025

AN EXAMINATION OF 2SLGBTQIA+ PSYCHOLOGICAL WELLBEING IN CANADA AND THE UNITED STATES

2023· article· en· W4384568705 on OpenAlexaffabout
Patrick Hickey, Lisa A. Best, David Speed

Bibliographic record

VenuePsychological applications and trends · 2023
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of New BrunswickDalhousie University
Fundersnot available
KeywordsPsychologyComputer science

Abstract

fetched live from OpenAlex

Compared to the general population, members of the 2SLGBTQIA+ community demonstrate lower mental health, psychological wellbeing, and life satisfaction (Conlin et al., 2019).An online questionnaire was administered to 534 participants across Canada (59.5%) and the United States (40.5%) to assess psychological wellbeing across sexual orientation and gender identity.Respondents were separated into four distinct categories to identify group differences: sexual minority (44.6%), gender minority (4.5%), double minority (sexual + gender minority; 26%), and non-minority (24.9%).A series of Chi-square tests and analyses of variance (ANOVA) were used to identify differences across groups and region.2SLGBTQIA+ participants reported significantly higher anxiety, depression, and loneliness (family, social) as well as lower life satisfaction than non-2SLGBTQIA+ participants.Further, significant group differences were found on some psychosocial measures; for example, double minority participants reported the lowest satisfaction with life and highest family loneliness relative to both sexual and non-minority categories.Overall, there were no differences between 2SLGBTQIA+ participants in Canada and the United States.Results demonstrate the continued disparity between 2SLGBTQIA+ and non-2SLGBTQIA+ populations in psychological wellbeing, with some poorer outcomes for double minority participants and limited differences between 2SLGBTQIA+ participants in Canada and the United States.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.388
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2023
Admission routes2
Has abstractyes

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