A qualitative study on health stigma and discrimination in the first year of the COVID-19 pandemic: Lessons learnt from a public health perspective
Bibliographic record
Abstract
Background: Stigmatisation, misinformation and discrimination have been magnified globally due to the COVID-19 pandemic. The healthcare sector was not spared from this. We conducted a transnational study, using the Health Stigma and Discrimination framework (HSDF) to explore public perception and reactions to the COVID-19 pandemic in a multicultural context. Findings from the Asian arm of the study, sited in Singapore, are reported in this paper. Methods: This phenomenological research deployed semi-structured informant interviews using non-probability sampling approaches to recruit members of the public. Interviews were coded independently by two researchers and thematic analysis was used to analyse the responses. Results: Twenty-nine members of the public (23-80 years old) were interviewed between Oct 2020 to Feb 2021. Five major themes were identified: (i) perception of stigma amongst respondents, (ii) experiences of stigma amongst respondents, (iii) views on what drove stigma and misinformation, (iv) facilitators in preventing and reducing stigma and misinformation, and (v) ageist attitudes towards older adults. Overall, construction workers living in dormitories, healthcare workers, and to some extent tourists from China, were perceived to have been stigmatised and shunned by the public. Place-based stigmatisation was common; participants responded by avoiding places that had confirmed cases of COVID-19. Perceived stigma was temporary and not enduring, driven at the outset by fear of being infected. This study also identified the role played by trust in reducing stigmatisation. The relative absence of politicising of issues and high-quality information readily disseminated to the public were reported as factors that could have reduced and prevented stigma and misinformation on the various groups. Ageist attitudes were observed in some participants with older adults being labelled as vulnerable, susceptible to misinformation and being less able to cope during the pandemic. Conclusion: Through the lens of the HSDF, this study provided an exploratory account of the nature of stigma that resulted from the COVID-19 pandemic in an Asian context. It also shed light on facilitators in preventing and reducing stigma during an outbreak especially the role of trust and communications during a public health crisis.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".