What Clinical Practitioners Need from Leadership to Promote Resilience and Mitigate Risks to Counsellors in a Post-Secondary Environment
Bibliographic record
Abstract
This dissertation examines what clinical practitioners need from leadership to foster resilience and mitigate risks like burnout, compassion fatigue, and vicarious trauma in post-secondary counselling environments. As student mental health demands surge—especially post-COVID—counsellors are stretched thin, managing increasingly complex cases with limited institutional support. While these professionals are trained to provide care, they often lack the same level of care from their workplaces, leaving them vulnerable to emotional exhaustion, disengagement, and burnout. Despite their essential contributions, counsellors face systemic barriers, inadequate institutional support, and leadership gaps that exacerbate workplace stress. This study amplifies the voices of post-secondary counsellors and clinical leaders from various Canadian institutions using a qualitative, phenomenological approach with narrative inquiry. Twelve participants—eight counsellors and four clinical leaders—shared their experiences, shedding light on the structural and leadership gaps that impact their well-being. Thematic analysis, supported by NVivo, revealed key trends: a lack of trauma-informed leadership, unsustainable caseloads, unrealistic institutional expectations, and limited opportunities for professional growth. Findings highlight that effective leadership is not just about policies—it’s about people. When leaders prioritize counsellor well-being, staff retention improves, service quality strengthens, and students receive better care. The research underscores the need for leadership models beyond traditional frameworks, incorporating trauma-informed, feminist, and servant leadership approaches that create psychologically safe workplaces. Recommendations include reducing caseloads, implementing structured peer support, offering flexible work options, and embedding professional development into institutional priorities. The study also introduces the Counsellor-Centered Leadership Assessment Model (CCLAM), a tool designed to help institutions evaluate
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".