The Strategies Sport Fans Used to Cope with the COVID-19 Pandemic Lockdown of Sporting Events
Bibliographic record
Abstract
When the COVID-19 pandemic shut down occurred, virtually all sports leagues—from recreational sports to professional leagues—were paused or canceled. This left a gap for fans of those sports to fill. The purpose of the present research study was threefold: 1) to examine what strategies sport fans used to cope with the loss of the live sport viewership/spectatorship; 2) determine how effective fans believed those coping mechanisms to be; and 3) examine fans’ behavioral intentions once sports were allowed to resume. Participants were recruited via a snowball sample and the Amazon MTurk platform. A total of 384 sport fans responded to the survey. While not all participants responded to all items, 168 indicated coping mechanisms for dealing with not watching sports and 219 reported coping mechanisms for not attending sports. The most common coping mechanism was watching old sporting events on television or via the internet. These mechanisms were reported to be very effective in helping participants cope with the loss of sports (M = 5.76, SD = 1.68 on a 1 to 8 scale). These findings provide support for the Team Identification – Social Psychological Health Model and suggest areas for interventions for sport marketers who are looking to maintain fans’ loyalty during future shutdowns of sport seasons, or other instances of missed sporting events.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".