Acceptability and perceptions of end-users towards an online sports-health surveillance system
Bibliographic record
Abstract
AIM: To describe the acceptability and the perceptions of athletes and staff members (ie, end-users) towards an online sports-health surveillance system. METHODS: A pilot study with a mixed-methods approach was pursued. Descriptive analysis was conducted to present the adherence of judo (n=34), swimming (n=21) and volleyball (n=14) athletes to an online registration of their sport exposure and any health complaints between April 2014 and January 2015. End-users' perceptions towards the system were investigated qualitatively with semistructured interviews (n=21). Qualitative analysis was based on the constant comparative method using principles of the grounded theory. RESULTS: The response rates of judo, swimming and volleyball athletes were 50% (SD 23), 61% (SD 27) and 56% (SD 25), respectively. Most athletes found it simple to register their sport exposure and health complaints online; however, personal communication was still preferred for this purpose. The system facilitated the communication between medical and trainer staff, who were able to identify in the system reports health complaints from athletes that were not necessarily communicated face-to-face. Therefore, staff members reported that they were able to intervene earlier to prevent minor health complaints from becoming severe health problems. However, staff members expected higher adherence of athletes to the online follow-ups, and athletes expected to receive feedback on their inputs to the system. CONCLUSION: An online system can be used in sporting settings complementary to regular strategies for monitoring athletes' health. However, providing feedback on athletes' inputs is important to maintain their adherence to such an online system.
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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".