Interpreting the Effects of the COVID-19 Pandemic: Bridging Psychological and Sociological Perspectives
Bibliographic record
Abstract
Recently, sociologists and psychologists have been investigating the implications of the COVID-19 pandemic, yet much of the social science literature regarding COVID-19 remains partial towards either the sociological or psychological perspective. To mitigate the effects of stigma and guilt, a holistic perspective that integrates sociological and psychological viewpoints needs development. The purpose of this article is to synthesize evidence on the social and psychological implications of the COVID-19 pandemic. In this context, the author focuses on two key themes, stigma and guilt. The concept of guilt is emphasized by the psychological literature, while, on the other hand, the concept of stigma exists both in sociology and psychology, but tends towards sociological interpretations given its historical origin. Overall, the presence of stigma and excessive guilt are associated with decreased social compliance and increased mortality due to the COVID-19 pandemic. The author argues that social practices that focus on inclusiveness and preparedness towards mitigating the effects of stigma and guilt—while also complying with public health measures—are crucial for social compliance and increasing societal well-being.
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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| 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".