The Role of Support Following Workplace Harassment Experiences
Bibliographic record
Abstract
Experiences of harassment and violence within the workplace in Canada are an increasingly serious concern. Three-quarters (71.4%) of Canadian workers in a recent survey experienced harassment and/or violence at work in the past year (Berlingieri et al., 2022). Following harassment and violence at work, individuals experience a wide range of negative consequences including mental health issues, physical health issues, and depleted social support networks. Through semi-structured interviews and thematic analysis, this study explored the role that support (including social, familial, and organizational) played following experiences of harassment and violence at work. Work environments are continuously perpetuating unhealthy and harassing behaviours, through a lack of support for victim-survivors. These individuals received support from those both within and outside of the workplace, which aided in feelings of validation and understanding. These supports, however, were not enough to change the toxic workplace cultures that perpetuate feelings of secrecy and continue to allow these harassing and violent behaviours to continue to occur. These participants advocated for a change in policy, reporting procedures, and workplace cultures, to ensure that victim-survivors do not have to continue to live and work with the fallout of the harassment and/or violence that they endured.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".