Racism and harassment towards frontline workers: experiences of environmental public health professionals during the COVID-19 pandemic response
Bibliographic record
Abstract
The COVID-19 pandemic has highlighted several challenges for Environmental Public Health Professionals (EPHPs). This study aims to understand the experiences of EPHPs during the pandemic to improve future crises and incidents that may arise. A mixed-methods, cross-sectional online survey was conducted in June 2021. Frequency tabulations were used to analyze close-ended survey responses, and both a conventional content analysis and thematic analysis were conducted on open-ended responses. A total of 80 eligible survey responses were received. Most respondents were located in Ontario (53.8%). Study results revealed that EPHPs have faced incidents of harassment, frustration from the public, and a lack of support from management. These matters ultimately challenged the well-being of EPHPs, placing them at increased risk of burnout, stress, and fear. Thus, it is crucial that support for mental health and reporting systems is improved for the future to ensure that EPHPs are able to meet the demands of their work. Further studies should be conducted to examine the lived experiences of EPHPs and barriers faced in more detail, including possible strategies to improve their working environments and well-being.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".