‘First, do no harm’: conducting research on interpersonal violence in sport
Bibliographic record
Abstract
It was Hippocrates who stated ‘First, do no harm’, and we believe the sentiment of this medical principle is one that every researcher should embody when conducting research on interpersonal violence (IV) in sport. Conducting such research presents unique considerations for researchers, particularly in relation to supporting participant and researcher well-being. Similarly, approaching this sensitive topic with clear definitions of IV in sport and an understanding of trauma- and violence-informed care (TVIC) is paramount to the protection and care of everyone involved in the research. The landscape for researching IV in sport that has rapidly emerged to investigate its determinants from various lenses including ethical, sociological, psychological, criminological and organisational.1 2 The siloed nature of research on IV in sport also has led to inconsistency in terminology, hindering effective communication and collaboration within and outside the field. The terms used to describe and study IV in sport have a profound impact on how the problem is communicated and understood. The use of diverging definitions creates challenges for determining what is within or outside the scope of research, limits our ability to meaningfully compare prevalences and experiences reported across projects, and impacts methodological considerations such as the effective recruitment of participants. Currently, terms such as maltreatment, non-accidental violence, harm, harassment, abuse and IV are used interchangeably, and clarification on the use of terminologies is essential. This paper aligns with the latest International Olympic Committee (IOC) Consensus Statement1 and the WHO’s typology of violence3 and focuses on ‘ interpersonal violence’, which differs from self-directed and collective violence (box 1). Box 1 ### Glossary with operationalised definitions of interpersonal violence in sport
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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 | 0.256 | 0.345 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.051 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".