Staying safe while staying together: the COVID‐19 paradox for participants returning to community‐based sport in Victoria, Australia
Bibliographic record
Abstract
OBJECTIVE: To identify the challenges adult community sport participants anticipate when returning to sport in Victoria, Australia, post a COVID-19 shutdown. METHODS: Using online concept mapping, participants brainstormed challenges to returning to community sport, sorted them into groups and rated them for impact and ability/capacity to overcome. Analysis included multidimensional scaling and hierarchical cluster analysis. RESULTS: Forty-five community sport participants representing 24 sports identified 69 unique challenges to returning to sport. Eight clusters/questions participants need answered emerged from the sorting data (mean cluster impact and ability/capacity rating out of 5): Will we have enough participants? (3.32, 2.89); How do we stay safe? (3.31, 3.35); How will our sport change? (3.17, 2.85); How can we stay together? (3.15, 3.01); Will I be physically ready? (3.15, 3.05); What about the money? (2.86, 2.53); What about me? (2.65, 3.13); and What about the facilities? (2.49, 2.45). CONCLUSIONS: Participants perceived paradoxical challenges to returning to sport after COVID-19 shutdown, which revolved around staying safe, staying connected and accessing meaningful sport activities. Implications for public health: Sport organisations and public health practitioners should address the participant-centred challenges identified in this study to maximise the public health benefits of participants returning to community sport.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| 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".