From the Screen to the Streets: Technology-Facilitated Violence Against Public Health Professionals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it