What contributes to COVID-19 online disinformation among Black Canadians: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Black Canadians are disproportionately affected by the COVID-19 pandemic, and the literature suggests that online disinformation and misinformation contribute to higher rates of SARS-CoV-2 infection and vaccine hesitancy in Black communities in Canada. Through stakeholder interviews, we sought to describe the nature of COVID-19 online disinformation among Black Canadians and identify the factors contributing to this phenomenon. METHODS: We conducted purposive sampling followed by snowball sampling and completed in-depth qualitative interviews with Black stakeholders with insights into the nature and impact of COVID-19 online disinformation and misinformation in Black communities. We analyzed data using content analysis, drawing on analytical resources from intersectionality theory. RESULTS: = 30, 20 purposively sampled and 10 recruited by way of snowball sampling) reported sharing of COVID-19 online disinformation and misinformation in Black Canadian communities, involving social media interaction among family, friends and community members and information shared by prominent Black figures on social media platforms such as WhatsApp and Facebook. Our data analysis shows that poor communication, cultural and religious factors, distrust of health care systems and distrust of governments contributed to COVID-19 disinformation and misinformation in Black communities. INTERPRETATION: Our findings suggest racism and underlying systemic discrimination against Black Canadians immensely catalyzed the spread of disinformation and misinformation in Black communities across Canada, which exacerbated the health inequities Black people experienced. As such, using collaborative interventions to understand challenges within the community to relay information about COVID-19 and vaccines could address vaccine hesitancy.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.033 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".