Psychosocial Impacts of the COVID-19 Pandemic
Bibliographic record
Abstract
Background and objective: Globally, men are at greater risk of mortality and serious physical consequences from COVID-19 infection than women, but are less impacted by the pandemic’s impact on labor force participation and increased childcare responsibilities. Outside of gender identity, however, it is unclear whether men’s beliefs about gender may be related to the kinds of COVID-19-related impacts they report. This study sought to describe the employment, income, and household responsibility-related impacts of the pandemic on a sample of young men in the U.S. and to examine relationships between the men’s gender ideologies and attitudes toward gender equity with self-reported stress impacts of the pandemic. Methods: The data are from an online survey of 481 young men from across the U.S. Measures included scales assessing masculinity ideology, modern sexism, support for traditional divisions of labor by gender, and attitudes toward gender equity. New items developed for this study assessed COVID-19-related changes in employment, household responsibilities, and childcare duties as well as levels of stress. Hierarchical regression examined the relative roles of demographic characteristics, changes in employment and household work, and gender-related attitudes on COVID-related stress. Results: Descriptive findings showed that under 50% of the men in the sample experienced negative COVIDrelated impacts on employment, but that a majority of the men reported at least some COVID-related stress. Results of the hierarchical regression suggest that higher levels of stress were predicted by having a minoritized sexual identity, less religiosity, experiencing employment or household responsibility-related changes, and not endorsing modern sexism or a traditional, gendered division of labor. Conclusions: Experiencing COVID-19-related stress was normative in this sample of young men. However, endorsing traditional notions of a gendered division of labor was slightly protective against higher levels of COVID-related stress. These findings add to existing evidence that gender analysis must be a central component of ongoing COVID-related policy and programming development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".