Mental well-being, social support, and the unique experiences of transgender and nonbinary people during the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic introduced a myriad of novel stressors, and early research suggests the mental well-being of many has suffered as a result. Transgender and nonbinary (TN) people in particular may have experienced additional stressors related to their gender identity, and may not have had access to minority coping resources that could normally buffer against experiencing negative mental health outcomes. In May 2020, 1160 cisgender heterosexual, 369 cisgender lesbian, gay, bisexual, and queer (LGBQ), and 195 TN people completed a survey on their mental well-being and experiences during the first wave of the COVID-19 pandemic. We also asked TN participants about how their lesbian, gay, bisexual, transgender, and/or queer (LGBTQ+) identity intersected with their experience of the COVID-19 pandemic. We found that TN participants experienced more psychological distress and less social support than cisgender heterosexual participants during the COVID-19 pandemic. We also found that social support was associated with less psychological distress among TN participants during the pandemic; however, LGBQ+ and TN community connectedness were not related to distress. Using inductive thematic analysis, we summarize TN participants’ descriptions of the ways that their LGBTQ+ identities intersected with the pandemic to change their access to gender-affirming services and behaviours, their home and public life, and their experiences of affirming social support and/or LGBTQ+ community connectedness. Together, our findings provide valuable insights into the experiences of TN people during the pandemic and highlight the ways in which our “normal” society is difficult for TN people to inhabit.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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 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".