Exploring perceptions and experiences of stigma in Canada during the COVID-19 pandemic: a qualitative study
Bibliographic record
Abstract
Background: The COVID-19 pandemic fueled stigmatization and discrimination, particularly towards individuals of Chinese or East Asian ethnicity. We conducted interviews with members of the public in Canada in order to describe and understand stigma perceptions and experiences during the COVID-19 pandemic. Methods: We used a phenomenological approach to describe stigma experiences of Canadian residents during the COVID-19 pandemic and compared the stigma perceptions and experiences of East Asian and non-East Asian individuals. Participants were invited to take part in a single, semi-structured interview. The interview guide was rooted in the Health Stigma and Discrimination Framework (HSDF). Interviews were conducted in English, Mandarin, and Cantonese. Following participant consent, interviews were audio recorded and transcribed verbatim. Data were double coded and analyzed using qualitative content analysis guided by a framework approach. Results: A total of 55 interviews were conducted between May and December 2020. Fifty-five percent of the sample identified as East Asian, 67.3% identified as women, and mean age was 52 years (range 20-76). Fear of infection, fear of social and economic ramifications, and blame for COVID-19 were reported drivers of stigma. Participants described preexisting perceptions on cultural norms and media influence as facilitators of stigma that propagated harmful stereotypes, particularly against Chinese and East Asian individuals. Participants observed or experienced stigmatization towards place of residence, race/ethnicity, culture, language, occupation, and age. Stigma manifestations present in the public and media had direct negative impacts on East Asian, particularly Chinese, participants, regardless of whether or not they personally experienced discrimination. Conclusions: We used the HSDF as a rooting framework to describe perceptions and impact of stigma, particularly as they related to race/ethnicity-based stigmatization in Canada. Participants reported a number of drivers and facilitators of stigma that impacted perceptions and experiences. These findings should be used to develop sustained strategies to mitigate stigma during public health emergencies or other major crises. Supplementary Information: The online version contains supplementary material available at 10.1186/s44263-023-00020-7.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.028 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".