Courageous conversations: The possibilities and practicalities of discussing death when teaching gerontology
Bibliographic record
Abstract
When developing curricula in gerontology related to families and health, we often relegate death and dying, end-of-life care, and bereavement to the last topic of the course. However, what if we were to restructure our classes to consider death and dying first? This paper explores pedagogical and practice-based strategies for integrating death education into gerontology curricula from the outset. Guided by the Compassionate Communities approach to palliative care (Kellehear, 2005), which emphasizes that experiences of death, dying, and bereavement extend beyond professional domains, we argue that gerontology educators are uniquely poised to support public engagement with mortality. Engaging with these topics early in the curriculum encourages reflection on death, finitude, and grief, benefiting both students and instructors. We ask: how can this be done effectively? As educators, we too need to learn how to engage with this topic meaningfully and become comfortable with discomfort. Drawing from our own teaching experiences, we highlight how tools, such as the arts and Death Cafés can provoke critical insights on how grief and death inform the life course. An online appendix of resources is provided to support instructors in teaching students about death and exploring their own relationship to mortality as part of this process.
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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 | 0.032 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".