Bibliographic record
Abstract
Abstract Pandemics are global outbreaks of novel or re-emerging infectious diseases. Pandemics will likely become more prevalent in the coming years due to climate change, the growing global population, and other reasons. Pandemics reveal aspects of humanity rarely seen in calmer times. Psychology plays an essential role in pandemics, in which people’s beliefs, emotions, and behaviors influence disease transmission, infection-related mental health problems, and societal disruption. Uncertainty is an inherent aspect of pandemics. When faced with novel pathogens, people cope with these invisible, uncertain threats in various ways, including coping strategies that provide only an illusion of control, making people calmer but not safer. Other psychological phenomena observed during pandemics include fear extremes (e.g. excessive fear vs. undue disregard for the threat), fleeing, panic buying, xenophobia, rumors and conspiracy theories, protests about wearing protective face masks, anti-vaccination attitudes, lockdown protests, increases in mood and anxiety disorders, and other problems. Efforts to manage one problem (e.g. lockdown to stem the spread of infection) may worsen others (e.g. mental health problems). The present volume offers an in-depth analysis of these and other issues concerning the psychology of pandemics. The book explores promising new directions for maintaining and improving mental health and enhancing adherence to pandemic mitigation measures. This book is intended for those working in psychology, healthcare, public health, and related fields—clinicians, researchers, policymakers, and students—as well as the general reader. To prepare for future global outbreaks of infectious diseases, we all would benefit from a better understanding of the psychology of pandemics.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".