The Hazards of Daily Stressors: Comparing the Experiences of Sexual and Gender Minority Young Adults to Cisgender Heterosexual Young Adults During the COVID-19 Pandemic
Bibliographic record
Abstract
Some individuals may be at greater risk for encountering stressors in daily life than others, especially those with minority identities. Initial evidence shows that the disparities between cisgender heterosexual (CH) individuals and sexual and gender minority (SGM) individuals on stress-related experiences may be exacerbated by the COVID-19 pandemic. We examined the daily stressors experienced by undergraduate students during the COVID-19 pandemic (stressor exposure), the association between the experience of daily stress and same-day negative mood (stressor reactivity), and whether these varied between undergraduate students with SGM identities and their CH counterparts using a 14-day daily diary design. We did not find significant differences between SGM and CH groups on stressor exposure or stressor reactivity. One common feature of daily diary data is right censoring, which is when some individuals do not experience specific events during the study duration. We used multilevel survival analysis, which accounts for right censored data, to examine group differences in the risks of stressor exposure. We discuss the statistical issues involved when right-censored cases are not taken into consideration in studies of stressor exposure and propose multilevel survival analysis as one solution to move the field towards more accurately understanding whether, when, and why SGM individuals are at greater risk for stressors.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".