Everyday discrimination, daily affect, and physical symptoms during the COVID-19 pandemic.
Bibliographic record
Abstract
OBJECTIVE: Abundant evidence has linked everyday discrimination with health risks. Because the COVID-19 pandemic has increased exposure to discrimination (e.g., based on age and race), it is important to understand the day-to-day implications of discrimination experiences for well-being. Furthermore, daily positive events were examined as a moderator due to their potential for mitigating the associations between everyday discrimination and well-being. METHOD: From March to August 2020, 1,212 participants aged 18-91 in the United States and Canada (84% women, 75% White) completed surveys for seven consecutive evenings about everyday discrimination, positive events, physical health symptoms, and positive and negative affect. Data were analyzed using multilevel models and controlled for sociodemographic factors. RESULTS: Everyday discrimination was reported on 9.3% of days when in-person or remote social interactions occurred. Within-persons, positive affect was lower and negative affect and physical symptoms were higher on days when discrimination occurred versus on days without discrimination. Positive events mitigated the within-person association between everyday discrimination and same-day negative affect, but not for positive affect or physical symptoms. Discrimination perceived to be due to age was associated with higher negative affect and lower positive affect within-persons. Positive events did not moderate the associations between age-based discrimination and same-day outcomes. CONCLUSIONS: Everyday discrimination was related to lower daily positive affect and higher negative affect and physical symptoms during the COVID-19 pandemic. This study provides initial evidence that daily positive events partially offset the increased negative affect associated with same-day discrimination. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.000 | 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".