Unveiling the Layers of Faculty Burnout in Canadian Private Universities during the post-COVID Pandemic Period
Bibliographic record
Abstract
The COVID-19 pandemic significantly altered the employment landscape, including educational institutions, creating long-term effects for college and university employees. One of the major implications of the pandemic is the systemic transformations in employment and organisational dynamics, that resulted in effects on working conditions and workplace responsibilities. Emotional, physical, and mental exhaustion accompanying these changes have heightened the risk of faculty burnout. This qualitative research, reports on the multifaceted phenomenon of faculty burnout within Canadian private universities amidst the post pandemic. A phenomenological approach was used to conduct indepth interviews with participants, resulting in narrative responses regarding experiential aspects of participants’ workplaces, daily routines, social interactions, and perceptions of organisational support. Notable findings included burnout in eleven distinct categories: technology and tools, career development, intrinsic motivation, psychological factors, social worth, unmet individual expectations, mental and physical health, time constraints, task management, workload, and professional role. The findings were categorized into three overall themes, including organisational factors, individual factors, and work-specific factors. A comprehensive conceptual model was developed to provide a snapshot of the value alignment of these factors to the overall concept of faculty burnout. The results highlight the significance of the intricate interplay of various elements contributing to burnout and offer significant insights into the lived experiences of faculty members within the context of burnout among Canadian private universities during and after the pandemic. The study informs academic researchers and institutional policymakers of the need for adaptable and responsive interventions to support the faculty, especially in the post-Covid context.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.028 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".