Queering Public Health: A Critical Examination of Healthcare Access and Gender Expression among Trans, Nonbinary, and Other Gender Nonconforming People during COVID-19
Bibliographic record
Abstract
Research documenting the impact of COVID-19 on Two-Spirit, lesbian, gay, bisexual, transgender, and queer (2SLGBTQ+) populations in Canada is limited. Our objectives were to investigate the impact of COVID-19 lockdown measures on the lives of trans, nonbinary, and other gender nonconforming (TGNC) people. Engage COVID-19 is a mixed methods study examining the impact of COVID-19 on gay, bisexual, queer, and other men who have sex with men (GBQM) living in Vancouver, Toronto, and Montreal, Canada. Using purposive sampling, we conducted in-depth qualitative interviews (between November 2020–February 2021 and June–October 2021) with 93 participants who discussed the impact of COVID-19 on their lives. Seventeen participants were identified as TGNC. TGNC participants reported barriers to trans healthcare during the initial months of the COVID-19 pandemic. Several participants indicated that some public health interventions during COVID-19 (i.e., lockdowns) eased the pressure to “perform” gender due to fewer in-person interactions. During lockdowns, TGNC participants increasingly cultivated community networks online. Nevertheless, participants reported longing for the social support that was available to them during pre-COVID. Lack of access to community spaces during lockdowns had a negative impact on participants’ mental health, despite reduced pressure to perform gender and opportunities for social engagement in online spaces.
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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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.020 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".