P-516 WORKING IN THE TIME OF COVID-19: HOW COVID-19 PUBLIC HEALTH POLICIES AFFECT THE EXPERIENCE OF BULLYING AND HARASSMENT AMONG RESTAURANT WORKERS
Bibliographic record
Abstract
Abstract Introduction This study explored how COVID-19 public health policies affected experiences of bullying and harassment among workers in the restaurant industry and the impact of these policies on the health and safety of workers. Methods Qualitative interviews were conducted with 47 restaurant workers in the Canadian province of British Columbia. Participants worked in various roles at any kind of restaurant during the first two years of the COVID-19 pandemic. Ethics approval was obtained from the UBC Research Ethics Board. Inductive coding was applied towards the development of themes. Results COVID-19 public health restrictions in restaurants contributed to bullying and harassment of workers. A lack of managerial support and implementation guidelines from public health officials led to workers having to enforce restrictions on their own, which resulted in workers routinely experiencing verbal violence and harassment by customers. Workers described unsafe work conditions that put them at risk for contracting COVID-19 as well for bullying and harassment from management, which was exacerbated for women, gender-diverse, and racialized workers. In workplaces with a supportive organizational culture, workers reported feeling safer and buffered from incidents of bullying and harassment from customers.“ Discussion The COVID-19 pandemic exacerbated existing structural dynamics in the restaurant industry that enabled bullying and harassment of workers, including the hierarchical dynamic of restaurant work as well as discrimination in the industry. Conclusion While supportive workplace policies and practices mitigated some of the bullying and harassment experienced by restaurant workers working during the COVID-19 pandemic, broader equity-focused organizational and structural change is needed to protect workers.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".