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When a client dies: preparing social work students for mortality-related challenges in field practicum

2024· article· en· W4402961388 on OpenAlexaff
Oleksandr Kondrashov

Bibliographic record

VenueSocial pedagogy theory and practice · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsPracticumField (mathematics)Social workWork (physics)SociologyPsychologyEngineeringMedical educationPedagogyMedicinePolitical scienceMechanical engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.145
GPT teacher head0.537
Teacher spread0.392 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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