Crisis Communication by School Leaders during the COVID-19 Global Pandemic
Bibliographic record
Abstract
Schools are not immune to crises. Whether it be earthquakes, wildfires, shootings, or global pandemics, schools will always be required to react quickly and efficiently to crises (Liou, 2015, p. 248). One large component of this reaction is communication. Therefore, school leaders need to be prepared to communicate quickly, efficiently, and effectively both internally and with the broader community during times of crisis. The coronavirus pandemic of 2020 created an exceptional urgency for schools to practice and refine their crisis communication as they dealt with the ongoing pandemic (Government of Canada, 2022). In British Columbia, the pandemic caused a state of emergency that has lasted nearly a year and a half (Lawson et al., 2021). During this time, schools went through many different situations of crisis, including short-term emergencies and long-term sustained stress. Schools also needed to react quickly to changing government guidelines, community exposures and public health directives (BC Ministry of Health, 2021). The purpose of this study is to examine the opportunities and challenges that arose as school leaders attempted to develop best practices, processes and procedures that amounted to effective communication during an unprecedented international health emergency.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".