A novel antidoping and medical care delivery model at the 2nd Summer Youth Olympic Games (2014), Nanjing China
Bibliographic record
Abstract
BACKGROUND: Antidoping and medical care delivery programmes are required at all large international multisport events. OBJECTIVE: To document and critique the novel antidoping and medical care delivery models implemented at the 2nd Summer Youth Olympic Games, Nanjing 2014. METHODS: The International Olympic Committee implemented two new models of delivery of antidoping and medical care at the YOG. A review of these models as well as the public health programme and two health educational initiatives in the Cultural and Educational Program was undertaken by the International Olympic Committee. RESULTS: The implementation of the new antidoping model was feasible in the setting of the YOG. The antidoping rules and regulations of the International Olympic Committee were respected. This model enhanced the educational initiative and provided financial as well as human resource savings. The execution of the hospital-based venue model of medical care delivery at the YOG was also feasible in this setting. This model provided a practical infrastructure for the delivery of medical care at multisport events with the goal of providing optimum athlete healthcare. A public health prevention programme was implemented and no public health risks were encountered by the participants or the Nanjing citizens during the YOG. Finally, the implementation of the athlete health educational programmes within the Cultural and Educational Program provided athletes with an opportunity to improve their health and performance. CONCLUSIONS: To achieve the goal of protecting athlete health, and of employing effective doping control and education, new alternate models of antidoping and medical care delivery can be implemented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".