Experiences of gender-diverse youth during the COVID-19 pandemic in Canada: A longitudinal qualitative study
Bibliographic record
Abstract
BACKGROUND: Recent studies have suggested that the COVID-19 pandemic has increased existing health challenges experienced by transgender (trans) and non-binary people. Additionally, COVID-19 has also negatively impacted youth mental health. However, the impact of the COVID-19 pandemic with respect to the intersection of youth and trans and non-binary populations is currently not well established. The present longitudinal qualitative study aimed to examine the evolving challenges experienced by trans and non-binary youth during the COVID-19 pandemic. METHODS: Gender-diverse youth, defined as participants who did not identify as cisgender in April 2020, were invited to participate from among the participant pool of a COVID-19 cohort study in Canada. Qualitative interviews were conducted in August 2020, January 2021, and August 2021, during the first year and a half of the COVID-19 pandemic. Qualitative themes were identified based on reflexive thematic analysis and plot-line narrative inquiry. RESULTS: Ten participants aged 18 to 28 were included in the present analysis, yielding 29 transcripts. We identified themes of (1) losses of connection to gender-diverse communities, (2) changes in gender identity, affirmation, and self-reflection, (3) a dual burden of trans and non-binary specific health and service access challenges as well as COVID-19 pandemic related health challenges, and (4) virtually rebuilding gender-diverse communities during the COVID-19 pandemic. CONCLUSIONS: Gender-diverse youth may experience unique challenges during the COVID-19 pandemic. The losses with gender-diverse communities may further isolate queer people from access to healthcare, housing, and employment. Public health policy targeted at gender-diverse youth may consider addressing "upstream" disparities in healthcare and housing in order to support the rebuilding of queer and gender-diverse communities by gender-diverse people.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.026 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".