Being there: A scoping review of grief support training in medical education
Bibliographic record
Abstract
INTRODUCTION: Medical education experts argue that grief support training for physicians would improve physician and patient and family wellness, and should therefore be mandatory. However, there is little evidence about the range of curricula interventions or the impact of grief training. The aim of this scoping review was to describe the current landscape of grief training worldwide in medical school, postgraduate residency and continuing professional development in the disciplines of pediatrics, family medicine and psychiatry. METHODS: Using Arksey and O'Malley's scoping review principles, MEDLINE, EMBASE, ERIC, PsychInfo and Web of Science were searched by a librarian. Two levels of screening took place: a title and abstract review for articles that fit a predefined criteria and a full-text review of articles that met those criteria. Three investigators reviewed the articles and extracted data for analysis. To supplement the search, we also scanned the reference lists of included studies for possible inclusion. RESULTS: Thirty-seven articles published between 1979 and 2019 were analyzed. Most articles described short voluntary grief training workshops. At all training levels, the majority of these workshops focused on transmitting knowledge about the ethical and legal dimensions of death, dying and bereavement in medicine. The grief trainings described were characterized by the use of diverse pedagogical tools, including lectures, debriefing sessions, reflective writing exercises and simulation/role-play. DISCUSSION: Grief training was associated with increased self-assessed knowledge and expertise; however, few of the studies analyzed the impact of grief training on physician and patient and family wellness. Our synthesis of the literature indicates key gaps exist, specifically regarding the limited emphasis on improving physicians' communication skills around death and dying and the limited use of interactive and self-reflexive learning tools. Most trainings also had an overly narrow focus on bereavement grief, rather than a more broadly defined definition of loss.
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 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 | 0.026 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.026 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".