Identifying future research priorities in the field of interpersonal violence (IV) towards athletes in sport: a Delphi study
Bibliographic record
Abstract
OBJECTIVE: Our aim was to define the priorities for future research in safeguarding athletes from interpersonal violence (IV) in sport through a Delphi consensus study of researchers in the field. METHODS: An internet-based three-round Delphi method was used as a multistage facilitation technique to arrive at a group consensus (set at ≥75% agreement). A targeted literature search was conducted to develop a list of potential research priorities that were presented as short statements in the first round. RESULTS: A total of 52 participants (researchers in IV in sport) took part in the first round, 52 completed the second round and 44 completed the third round. Respectively, 47 items, 83 items and 60 items were included in each round. The participants achieved consensus on 11 statements in the first round, seven in the second round and 31 in the third round, for a total of 49 consensus research priorities. The first four priorities that reached consensus (78.8-80.8% agreement) directly following the first Delphi round were scored with high importance (between 6.2 and 6.3 on a scale of 7). Those four priorities included: (1) documenting the experiences of children athletes and minors, (2) studying the disclosure or reporting of violence, (3) developing, evaluating and advising on interventions targeting education and training and (4) documenting the experiences of violence of para athletes. CONCLUSION: This study defines research priorities for IV in sport that may elucidate further gaps in current policies and practices.
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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 | 0.133 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".