Death, dying, and bereavement in undergraduate dental education: A narrative review
Bibliographic record
Abstract
As the population ages, and bidirectional relationships between oral and general health become clearer, dentistry has to be prepared for the needs of older adults, including at end of life. Death does not only occur in geriatric populations however; death, dying and bereavement are issues that affect all patients and practitioners. Dental education is not preparing undergraduate students to meet clinical, spiritual, and psychosocial needs of patients and families requiring end-of-life care. Further, it does not prepare them for the emotional impact of death on their personal or professional lives. This review examines how death, dying, and bereavement could be integrated into undergraduate dental education. We conducted a narrative review using seven data bases, in English, up to 2018. We retrieved 159 papers, of which 36 were included and analysed thematically. The findings parse into two deductive and one inductive theme: 1. Supporting dental students experiencing death, dying, and bereavement; 2. Teaching death, dying and bereavement: curricula, content, and strategies; and 3. Fostering compassionate care in dental education. Health professions curricula are beginning to address how to support trainees experiencing death and dying in their personal lives and when working with patients and families. Dental education has been slow to adopt this trend. No robust studies addressing how best to educate and support learners and professionals were found. Future research should include an examination of what is currently included in training, and a study with educators and professionals to design how best to prepare learners in their training and practice.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".