Workplace violence in the COVID-19 era: a qualitative analysis of harassment and threats against public health professionals in Canada
Bibliographic record
Abstract
Objectives: This study reports the results of a qualitative study involving public health professionals and documents their experiences with cyberviolence, harassment and threats during the COVID-19 pandemic. Method and analysis: The research adopted a discovery-oriented qualitative design, using constructivist grounded theory method and long interview style data collection. Twelve public health professionals from across Canada who held responsibility for COVID-19 response and public health measures in their respective jurisdictions participated. Constant comparative analysis was used to generate concepts through inductive processes. Results: Data revealed a pattern that began with mainstream media engagement, moved to indirect cyberviolence on social media that fuelled outrage and polarisation of members of the public, followed by direct cyberviolence in the form of email abuse and threats, and finally resulted in physical threats and confrontation-which were then glorified and amplified on social media. The prolonged nature and intensity of harassment and threats led to negative somatic, emotional, professional and social outcomes. Concerns were raised that misinformation and comments undermining the credibility of public health professionals weakened public trust and ultimately the health of the population. Participants provided recommendations for preventing and mitigating the effects of cyber-instigated violence against public health professionals that clustered in three areas: better supports for public health personnel; improved systems for managing communications; and legislative controls on social media including reducing the anonymity of contributors. Conclusion: The prolonged and intense harassment, abuse and threats against public health professionals during COVID-19 had significant effects on these professionals, their families, staff and ultimately the safety and health of the public. Addressing this issue is a significant concern that requires the attention of organisations responsible for public health and policy makers.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.028 | 0.013 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.007 |
| 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".