Stigmatization in the context of the COVID-19 pandemic: a survey experiment using attribution theory and the familiarity hypothesis
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic has created a global health crisis, leading to stigmatization and discriminatory behaviors against people who have contracted or are suspected of having contracted the virus. Yet the causes of stigmatization in the context of COVID-19 remain only partially understood. Using attribution theory, we examine to what extent attributes of a fictitious person affect the formation of stigmatizing attitudes towards this person, and whether suspected COVID-19 infection (vs. flu) intensifies such attitudes. We also use the familiarity hypothesis to explore whether familiarity with COVID-19 reduces stigma and whether it moderates the effect of a COVID-19 infection on stigmatization. Methods We conducted a multifactorial vignette survey experiment (28-design, i.e., NVignettes = 256) in Germany (NRespondents = 4,059) in which we experimentally varied signals and signaling events (i.e., information that may trigger stigma) concerning a fictitious person in the context of COVID-19. We assessed respondents’ cognitive (e.g., blameworthiness) and affective (e.g., anger) responses as well as their discriminatory inclinations (e.g., avoidance) towards the character. Furthermore, we measured different indicators of respondents’ familiarity with COVID-19. Results Results revealed higher levels of stigma towards people who were diagnosed with COVID-19 versus a regular flu. In addition, stigma was higher towards those who were considered responsible for their infection due to irresponsible behavior. Knowing someone who died from a COVID infection increased stigma. While higher self-reported knowledge about COVID-19 was associated with more stigma, higher factual knowledge was associated with less. Conclusion Attribution theory and to a lesser extent the familiarity hypothesis can help better understand stigma in the context of COVID-19. This study provides insights about who is at risk of stigmatization and stigmatizing others in this context. It thereby allows identifying the groups that require more support in accessing healthcare services and suggests that basic, factually oriented public health interventions would be promising for reducing stigma.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".