From the Screen to the Streets: Technology-Facilitated Violence Against Public Health Professionals
Bibliographic record
Abstract
This qualitative study sought to explore the experiences of public health professionals in Canada who were targets of harassment, abuse, and threatening behavior during the COVID-19 pandemic. Public health professionals from across Canada who held responsibility for public health measures in their respective jurisdictions participated in in-depth interviews. Using constructivist grounded theory and constant comparative analysis a cycle of violence was identified. Results revealed that as infections and deaths due to COVID-19 began to rise across the globe, participants engaged in efforts to educate the public through mainstream media and social media. While education efforts were generally positively received at the onset of the pandemic, as collective frustration with public health restrictions rose and misinformation began to proliferate, social media fueled outrage and polarization, and public anger began to focus on public health officials. Harassment, abuse, and threats on social media were followed by threats delivered through telephone and paper mail, and finally direct physical threats and confrontation—which were then glorified and amplified on social media. As reported by others, harassment and abuse were particularly virulent for public health professionals who were women or visible minority individuals. We conclude that the pattern of abuse identified in this study is reminiscent of the cycle of violence previously identified with respect to those who become radicalized on social media. These findings serve as a poignant example from which to develop guidelines for all professionals and researchers at risk of online abuse both in the health sector and beyond.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".