Chronic Disruptions and their Effects on Educational Leaders: COVID-19 Disruption in Post-Secondary Education
Bibliographic record
Abstract
This qualitative study explores how post-secondary education leaders perceive, experience, and respond to disruptions, using the COVID-19 pandemic as a key context. The research used a grounded theory approach to capture personal experiences and leadership insights to disruption. A total of 28 post-secondary education leaders, mostly from Western Canada were selected to participate through purposeful sampling. Data were gathered through World Café discussions, in-depth interviews, and expert reviews. Thematic analysis was used to identify key insights. Participants described disruptions as characterized by fear, uncertainty, and institutional struggles. The sudden move to remote learning revealed challenges in governance, decision-making, and crisis preparedness, along with political pressures. The crisis disruption also affected socio-emotional well-being, with both leaders and students experiencing stress, isolation, and anxiety. The growing need for mental health support highlighted the importance of stronger well-being initiatives. The study found that effective leadership during disruptions required adaptability, empathy, and quick decision-making. Leaders who embraced collaboration and flexibility were more successful, though the urgency of the crisis disruption sometimes made it hard to include all stakeholders in decision-making. Institutions had to quickly adopt new technology, including online learning and AI. While this improved access to education, it also raised concerns about engagement and quality. Leaders who encouraged innovation and adaptability during disruptions helped institutions adjust more effectively. The study emphasizes the need to improve leadership training, crisis management, and policy development in post-secondary. It also recommends institutions to strengthen mental health support, promote fairness, and advance digital learning while preparing for future disruptions. By focusing on adaptability and resilience, education leaders can build stronger, more sustainable learning environments.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".