#WhatWouldYouDo? A cross-sectional study of sports medicine physicians assessing their competency in managing harassment and abuse in sports
Bibliographic record
Abstract
Objectives To assess the clinical competence of sports medicine physicians to recognise and report harassment and abuse in sports, and to identify barriers to reporting and the need for safeguarding education. Methods We implemented a cross-sectional cohort study design recruiting through social media and international sports medicine networks in 2023. The survey captured participant perceptions related to the harmfulness of harassment and abuse. The survey incorporated the reasoned action approach as a theoretical framework to design survey questions to identify attitudes and self-efficacy to detect and report suspicions of harassment and abuse and to identify barriers to reporting. Results Sports medicine physicians (n=406) from 115 countries completed the survey. The situations of harassment and abuse presented in the survey were described by sports medicine physicians as having occurred in the 12 months before participating in the survey. Despite recognising the situations as harmful, sports medicine physicians were somewhat uncomfortable being vigilant for the signs and symptoms and reporting suspicions and disclosures of harassment and abuse (M=2.13, SD=0.67). In addition, just over one-quarter (n=101, 26.9%) was unaware of where to report harassment and abuse, and over half did not know (n=114, 28.1%), or were uncertain (n=95, 23.4%) of who the safeguarding officer was in their sports organisation. Participants identified many barriers to reporting harassment and abuse, including concerns regarding confidentiality, misdiagnosis, fear of reprisals, time constraints and lack of knowledge. Over half felt insufficiently trained (n=223, 57.6%), and most respondents (n=324, 84.6%) desired more education in the field. Conclusions Educational programmes to better recognise and report harassment and abuse in sports are needed for sports medicine trainees and practising clinicians. An international safeguarding code for sports medicine physicians should be developed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".