Bullying, harassment, and discrimination of musculoskeletal researchers and the impact of the COVID-19 pandemic: an international study
Bibliographic record
Abstract
PURPOSE: Bullying, harassment, and discrimination (BHD) are prevalent in academic, scientific, and clinical departments, particularly orthopedic surgery, and can have lasting effects on victims. As it is unclear how BHD affects musculoskeletal (MSK) researchers, the following study assessed BHD in the MSK research community and whether the COVID-19 pandemic, which caused hardships in other industries, had an impact. METHODS: A web-based anonymous survey was developed in English by ORS Spine Section members to assess the impact of COVID-19 on MSK researchers in North America, Europe, and Asia, which included questions to evaluate the personal experience of researchers regarding BHD. RESULTS: 116 MSK researchers completed the survey. Of respondents, 34.5% (n = 40) focused on spine, 30.2% (n = 35) had multiple areas of interest, and 35.3% (n = 41) represented other areas of MSK research. BHD was observed by 26.7% (n = 31) of respondents and personally experienced by 11.2% (n = 13), with mid-career faculty both observing and experiencing the most BHD. Most who experienced BHD (53.8%, n = 7) experienced multiple forms. 32.8% (n = 38) of respondents were not able to speak out about BHD without fear of repercussions, with 13.8% (n = 16) being unsure about this. Of those who observed BHD, 54.8% (n = 17) noted that the COVID-19 pandemic had no impact on their observations. CONCLUSIONS: To our knowledge, this is the first study to address the prevalence and determinants of BHD among MSK researchers. MSK researchers experienced and observed BHD, while many were not comfortable reporting and discussing violations to their institution. The COVID-19 pandemic had mixed-effects on BHD. Awareness and proactive policy changes may be warranted to reduce/eliminate the occurrence of BHD in this community.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".