Change in Finances, Peer Access, and Mental Health Among Trans and Nonbinary People During the COVID-19 Pandemic
Bibliographic record
Abstract
Purpose: Due to structural transphobia, trans and nonbinary (TNB) individuals were particularly vulnerable to the negative effects of social isolation and financial instability resulting from COVID-19. The present study examined the effect of change in finances and access to TNB peer gatherings on anxiety and depression during the COVID-19 pandemic. Methods: Participants were 18 years and older (mean = 30) and completed prepandemic baseline (Fall 2019) and pandemic follow-up (Fall 2020) surveys. Multivariable regressions examined associations between mental health and change in (1) finances and (2) access to TNB peer gatherings (in person or online). Results: Of 780 participants, 50% reported that the COVID-19 pandemic had a negative impact on personal income and 58.3% reported negative impact on access to TNB peer gatherings. Depression and anxiety symptoms increased from prepandemic to follow-up, and most participants were above measurement cutoffs for clinical levels at both time points. Change in finances and access to TNB peer gatherings interacted with prepandemic depression scores to predict depression symptoms during the COVID-19 pandemic. For participants with high prepandemic depression scores, financial stability predicted pandemic depression scores comparable to that predicted by negative financial change. No interaction was found between these variables when predicting anxiety symptoms during the COVID-19 pandemic. Conclusion: Findings underscore the influence of inequality and prepandemic mental health when considering the impact of COVID-19 on wellbeing. Results suggest need for multifaceted programs and services, including financial support and meaningful TNB community engagement, to address barriers to health equity posed by systematic gender oppression.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".