Addressing the Underrepresentation of Lesbian, Gay, Bisexual, Transgender, and Gender-Diverse Populations in the Canadian Longitudinal Study on Aging (CLSA)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The current research report examines the extent to which lesbian, gay, bisexual (LGB), and transgender and gender-diverse (TGD) older adults are represented in the Canadian Longitudinal Study on Aging (CLSA) relative to Canadian population data. To accomplish this aim, we descriptively and statistically compared LGB and TGD representation in the CLSA against national-level data from the 2023 Canadian Community Health Survey and 2021 Canadian Census. We found that bisexual people, transgender men, and transgender women were underrepresented in the CLSA (0.66% for bisexual people, and 0.01% each for transgender men and women) compared to national-level data (0.95% for bisexual people, 0.05% for transgender men, and 0.09% for transgender women). However, lesbian/gay and nonbinary participants were adequately represented in the CLSA (2.01% for lesbian/gay and 0.02% for nonbinary people) relative to national-level data (1.87% for lesbian/gay and 0.03% for nonbinary people). Varying assessment methods for sexual orientation and gender identity across CLSA waves complicate analyses but underscore evolving inclusivity efforts in longitudinal research. Based on our analyses, we detail recommendations for researchers using the CLSA to examine health outcomes and resilience factors among LGB and TGD populations, even with small sample sizes. Ultimately, these findings highlight the necessity of robust, inclusive data to inform interventions and support policy decisions for older LGB and TGD populations.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it