Stigma and fear during the COVID-19 pandemic: a qualitative study on the perceptions of healthcare workers in Canada and Singapore
Bibliographic record
Abstract
Introduction: We sought to explore healthcare providers (HCPs)' perceptions of and experiences with stigma during the COVID-19 pandemic in Canada and Singapore. Methods: We conducted a qualitative study (May 2020-February 2021) with HCPs in Canada and Singapore and developed a semi-structured interview guide rooted in the Health Stigma and Discrimination Framework (HSDF). We recruited participants online and through word of mouth via newsletters, blogs and social media. Participants were eligible to participate if they worked as a healthcare provider in Singapore or Canada during COVID-19. Following participant consent, data were recorded, transcribed verbatim, and coded using a framework approach. Coded data were charted into a framework matrix and used to compare themes in each country. Results: We conducted 51 interviews (23 in Canada; 28 in Singapore). HCPs perceived that patient fears coupled with mistrust of the health system impacted health behaviors. HCPs reported discrimination and stigmatization of population subsets. In Singapore, this included Chinese tourists and migrant workers and in Canada, this included people of Chinese ethnicity and people experiencing homelessness. This stigma was often attributed to pre-existing prejudices including perceptions that these populations were at increased risk of COVID-19 or not adhering to public health recommendations. HCPs feared spreading COVID-19 to family, peers and patients, often resulting in participants choosing to isolate from social circles. HCPs in both countries experienced occupation-based stigma, including stigma related to public health practices (masking, testing); in Canada, this intersected with race-based prejudice for participants of Chinese ethnicity. HCPs in both samples witnessed and experienced stigmatizing behavior; some participants also experienced discrimination. Conclusions: Secondary stigma related to occupation was experienced by HCPs during COVID-19. HCPs experienced intersecting stigma based on race/ethnicity and observed stigmatization of marginalized patient populations. Most themes were consistent across Canada and Singapore. Strategies to mitigate COVID-19 related stigma toward HCPs and at-risk patient populations are warranted.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.004 | 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".