Sexual harassment bystander training effectiveness: Experimentally comparing 2D video to virtual reality practice.
Bibliographic record
Abstract
Sexual harassment (hereafter SH) is a dysfunctional workplace behavior, resulting in negative outcomes for individuals, work groups, and organizations. Since #MeToo, companies have been innovating to increase the effectiveness of SH training by incorporating new content (e.g., bystander intervention skills) and new technology (e.g., virtual reality, hereafter VR). However, research has yet to determine the best practices or the effectiveness of these new innovations. The present study hypothesizes that SH bystander intervention training will be more effective when VR practice scenarios are used rather than two-dimensional (2D) video practice scenarios. We argue that the increased presence (i.e., the perception that people and places in a virtual simulation are real) afforded by VR should better replicate bystander experiences in real SH situations, thereby allowing trainees to develop bystander skills in a more realistic practice experience than 2D video provides. We experimentally test our hypothesis in a laboratory setting (N = 100). Our results show that the VR practice condition differed from the 2D video condition by increasing trainees’ intentions to engage in indirect, nonconfrontational, and widely applicable interventions (e.g., intervene by removing the target from the situation, approach the target to offer support later). However, our manipulation showed a negative effect on practice quantity (i.e., those in the VR condition explored fewer response options) and no effect on other operationalizations of training effectiveness (e.g., motivation to learn, knowledge, attitudes toward the training, intentions to directly confront the harasser, and intentions to formally report the harassment). Implications, limitations, and directions for future research are discussed.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".