Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19), a new viral illness that is part of the same family as the severe acute respiratory syndrome (SARS) coronavirus, has globally infected millions of people. The COVID-19 pandemic has created fear and anxiety within society and resulted in detrimental impacts such as social stigma toward certain groups. These groups include individuals who have contracted the virus, individuals of certain backgrounds, those associated with COVID-19 patients and healthcare providers. It is important to understand the process of stigma to develop more effective interventions; this can include utilizing a psychoeducational and behavioural modification approach to ease disease transmission and patient suffering. Globally, a collective effort needs to be made to increase education, improve the knowledge and attitudes related to COVID-19 and aid in the reduction of social stigma. Local and national teamwork and communication is important to work efficiently; transparency is key to alleviate fears and reduce stigma and discrimination by addressing general and specific concerns about COVID-19. Understanding stigma in the context of COVID-19 is essential to increase awareness of its negative consequences and to recognize that education can improve health care and outcomes for this disease.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".