When a client dies: preparing social work students for mortality-related challenges in field practicum
Bibliographic record
Abstract
Social work students frequently encounter issues of death, dying, grief and loss during field practicum placements, yet often feel unprepared to cope with the emotional challenges and provide effective care. A review of current literature reveals the significant impact of client death on social work students across diverse practicum settings, as well as substantial gaps in grief and loss education within social work curricula that leave students feeling ill-equipped to handle these experiences. This article argues that there is an ethical imperative to better support students facing mortality and loss in practicum by implementing trauma-informed, multi-level interventions. Key recommendations include: normalizing conversations about death, providing regular debriefing opportunities, helping students develop self-awareness and self-care plans, infusing grief and loss content into the curriculum, and facilitating meaning-making and resilience. Recommendations are offered for field instructors, faculty liaisons, placement agencies, universities, and students’ support systems to transform distressing encounters with client death into profound opportunities for personal growth, professional development, and building the capacity to provide compassionate end-of-life care. Ultimately, this article calls for a paradigm shift within social work education and society at large toward more a more grief-literate, death-positive culture that uplifts and humanizes the experiences of dying and bereaved individuals.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".