Safe and Healthy Para sport project (SHAPE): a study protocol of a complex intervention within Para sport
Bibliographic record
Abstract
Elite Para athletes report a high incidence of sports injuries, illnesses and other health issues. Despite this, there are few prevention programmes in Para sport, and many of the existing prevention programmes are not adapted to Para athletes. To improve the success of preventive measures, it has been suggested that sports safety work should facilitate health promotion, including athlete health education. Therefore, the overarching aim of this project is to evaluate an accessible health promotion web platform as part of a complex intervention that aims to improve knowledge of athlete health in Para sport. In this protocol, the development, future implementation and evaluation of the intervention are described. To inform the implementation and use of such interventions, it is recommended to involve end users in the development and implementation process. Therefore, a participatory design process, including athletes and the sports organisation, was used to develop an accessible health promotion web platform. To evaluate this complex intervention, a process evaluation combining quantitative evaluation assessing causal pathways with qualitative methods assessing multifaceted pathways will be used. The primary outcomes are injury/illness incidence, athlete health parameters, health literacy and user behaviour. A cohort of elite Para athletes (n=150) from Sweden and South Africa will be invited to participate. This project will be the first that aims to improve athlete health in Para sport through pragmatic and accessible health promotion. It is a boundary-crossing project that will be conducted in a real-world sport setting, including athletes with different socioeconomic backgrounds.
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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 | 0.033 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.008 |
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".