Experiences of COVID‐19 pandemic‐related stress among sexual and gender minority emerging adult migrants in the United States
Bibliographic record
Abstract
There is a dearth of research that examines COVID-19-related stress among multiply marginalised individuals who are in the developmental phase of emerging adulthood. This qualitative study investigated how the intersection of emerging adulthood, sexual and gender minority (SGM) identity, and migrant status were reflected in the experiences of SGM individuals (n = 37; ages 20-25 years old) who migrated to various parts of the United States in the last 5 years. Data were collected online using semi-structured interviews. Thematic analysis revealed that participants' developmental processes (e.g., identity exploration, building financial independence) were shaped by pandemic-related stressors, especially unemployment and financial instability. Participants who were able to maintain employment did so but at the risk of their health and safety. Findings also showed that participants experienced feelings of anxiety and depression due to social isolation, but online communication played an important role in combatting loneliness. Findings highlight the potential for trauma-informed and intersectional approaches to practice with SGM emerging adult migrants and expanded health services and temporary entitlement programs to mitigate the pandemic's effects on this population's psychosocial and financial well-being.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".