Death Literacy and Death Competence in Undergraduate Clinical and Allied Health Education: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: End-of-life care is a critical competency for the health care workforce, yet evidence suggests that many health care professionals feel unprepared to engage with death, dying, and bereavement. Death literacy and death competence are emerging frameworks for assessing readiness to provide high-quality, compassionate care. Although validated tools exist, little is known about the preparedness of final-year undergraduate health care students in Australia. Understanding their current levels of death literacy and death competence is essential for informing curriculum design and strengthening workforce capacity. OBJECTIVE: This study aims to (1) measure death literacy and death competence among final-year students in medicine, nursing, and allied health programs in Australian universities; (2) explore students' reflections on how undergraduate training has shaped their preparedness for end-of-life care; and (3) identify educational needs and opportunities for curriculum enhancement. METHODS: A mixed methods design will be used. An online survey (15-20 minutes) will be distributed to final-year students across multiple Australian universities. The survey includes the Death Literacy Index, the Death Competency Scale, and open-ended reflection questions. Quantitative data will be analyzed using descriptive and inferential statistics (in SPSS and Stata), with subgroup comparisons across disciplines and benchmarking against national professional datasets. Qualitative responses will be analyzed thematically. In phase 2, up to 20 students will participate in 2 focus groups (60-90 minutes each). The focus groups will explore survey findings and students' perceptions of training, preparedness, and gaps. Data will be transcribed, anonymized, and analyzed thematically using NVivo. RESULTS: Data collection for the national survey is scheduled from September 2025 to December 2025, with an anticipated sample of 60 to 120 final-year students across medicine, nursing, and allied health disciplines. Data analysis will begin in March 2026, and findings are expected to be published in late 2026. The findings will establish baseline measures of death literacy and death competence among final-year health care students and identify strengths and gaps in current curricula. Results will be synthesized to provide actionable insights for educators and to inform future intervention studies. CONCLUSIONS: By providing the first Australian pilot data on death literacy and death competence among final-year health care students, this study will inform curriculum development and workforce planning. The findings have the potential to enhance educational strategies, improve the preparedness of graduates for delivering end-of-life care, and contribute to the development of a death-literate health system. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/86867.
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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 | 0.077 | 0.055 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.081 | 0.017 |
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".