Virtual harassment: media characteristics' role in psychological health
Bibliographic record
Abstract
Purpose Using the stressor‐strain model and media richness theory, this study seeks to investigate the relationship between receiving a harassing message via computer‐mediated communication and psychological health. Design/methodology/approach A sample of 492 individuals completed an online questionnaire. Three media characteristics are examined as potential moderators: media richness, anonymity of the harasser, and location where the victim received the harassing message. Findings The results suggest that virtual harassment is associated with diminished psychological health (both directly and mediated by fear of future harassment), and each media characteristic plays a role in understanding the level of fear of future harassment. Anonymity and location moderate the mediator's (fear) role in the stressor‐strain model. Research limitations/implications This research addresses the need for explicit testing of the differentiating factors of various forms of workplace aggression as moderators. Specifically, media characteristics are relevant in the psychological experience of virtual harassment. Practical implications Virtual harassment appears to occur more frequently than face‐to‐face harassment, and often the two forms co‐occur. Implications for EAP counselors, computer usage and harassment policies are discussed. Originality/value This study is the first to examine how media richness, anonymity and location of harassing message impacts the individual outcomes of workplace non‐sexual virtual harassment. The results indicate that, while related to face‐to‐face harassment, virtual harassment appears to have more nuanced considerations for both practitioners and researchers.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".