Perceived crisis readiness of ice arena managers
Bibliographic record
Abstract
This correlational study examined the competency of crisis readiness and leadership of ice arena managers using a cross-sectional survey. The target audience was ice arena owners, managers, and operators of ice arenas in the United States and Canada. The researcher examined which factors (ice arena characteristics) best predict ice arena managers' Crisis Readiness as well as group differences in Crisis Readiness. The Crisis Readiness Survey, which consisted of 61 items, was emailed and/or posted to participants. Twelve questions gleaned demographical information, while the remaining items generated nine subscales: Emergency Evacuation Planning, Agency Calibration, Spectator Control, Policies and Procedures, Liability, Emergency/Crisis Management, Credential Control, Perimeter Control, and Crisis Leadership. The subscales were measured using a 5-point scale from 1 (very low capability) to 5 (very high capability). Subscales were calculated as the mean of respective items. The mean of 42 items generated the variable Overall Crisis Readiness. The data indicated statistically significant differences in Crisis Readiness by Participation in Training. The more training ice industry professionals obtain, the better prepared they will be to manage a crisis and lead their staff through it. Whereas Education Levels, Years of Experience, and Job Title did not generate statistically significant differences in Crisis Readiness subscales and overall. Participation in Training was found to be the best predictor of Overall Crisis Readiness, based on the predictive model Y = 2.43X + 2.59. However, Participation in Training only accounted for 5% variance in Overall Crisis Readiness. When comparing the United States and Canada, Canadian ice arena managers reported significantly higher competency in Evacuation Planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".