Bibliographic record
Abstract
Literature on the wellbeing of lesbian, gay, bisexual, transgender, and queer (LGBTQ) people has predominantly examined the negative experiences associated with LGBTQ identity; however, a growing body of literature explores the positive wellbeing of LGBTQ people. The present study examines social wellbeing as the connections across six elements identified in previous literature: discrimination, sense of safety, outness, social support, sense of belonging, and community acceptance. Latent profile analyses (LPA), a person-centered approach, was used to explore these elements of social wellbeing with cisgender LGBQ (n = 406) and transgender (n = 110) participants from a sample of LGBTQ individuals who completed an online survey in Waterloo Region, Ontario, Canada. Four distinct social wellbeing profiles were identified for LGBQ participants, and three profiles were identified for the transgender participants, with varying levels of social wellbeing represented. To further contextualize the profiles, identity and demographic covariates and self-esteem of each profile were assessed. This research demonstrates the value of LPA by contextualizing the distinct ways that LGBTQ people experience social wellbeing, providing guidance to develop services and policies to intentionally recognize the various profiles of people with diverse experiences within the Waterloo Region.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".