A Western Canadian University Leadership Response to Human-made and Natural Crises: Strategies and Challenges.
Bibliographic record
Abstract
This qualitative narrative research examined how senior higher education leaders in a large western Canadian university responded to human-made and natural crises. The literature review establishes a theoretical framework for crisis leadership in higher education examining prior research in leadership theories, crisis, crisis leadership, competencies, leadership and crises in higher education, crisis management processes in higher education, crisis communication, internally displaced people and support for crisis leaders. Purposive snowball sampling technique was used in conjunction with face-to-face semi-structured interviews, field texts, and online documents in this narrative inquiry. Semi-structured interviews were conducted with eleven senior leaders in a higher education institution, the city’s emergency response agency and an elementary school adjacent to the university. Participants were selected based on their positions, expertise and their active involvement in crisis management teams, and in certain internal processes such as preparation, planning and collaborations. The data from the study was presented in the form of rich thick descriptions to allow for deep, holistic, rich, and detailed understanding of the lived experiences of these leaders’ response to crises. My findings suggest that both the makeup of a crisis management team, and the leader’s prior crisis management experiences and competencies were crucial to handling any type of crisis. In addition, a higher education crisis competency model was created from the responses of crisis leaders. The crisis competency model may be potentially important for building trust to move an institution forward through crisis response. This model can be useful in training sessions for higher education leaders for developing an understanding of the types of competencies required for crisis response and management. The findings also suggest that higher education crisis leaders were not always attentive to taking care of themselves or getting support for their mental health and self-care after crisis response as they might have been. Without proper self-care and mental health care, crisis leaders could be susceptible to emotional triggers that could activate depression, fear, mental health concerns and sadness. Establishing protocols for personalized self-care and mental health care could prepare educational crisis leaders to deal with the next crisis they will most likely face.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".