An international survey of Death Doula training organizations: the views of those driving Death Doula training and role enactment
Bibliographic record
Abstract
Context: Death Doulas are working globally to provide non-medical end-of-life care. They have different training experiences and views on the role and whether it should be standardised. Objective: To seek the views of organisations responsible for training Death Doulas in order to determine what the drivers are behind this emerging role. Methods: We conducted an online survey with Death Doula training organisations in five countries utilising both a targeted and snowball approach. Qualitative analysis was undertaken with themes pre-determined (apriori) due to the nature of the survey categories. Results: In total, representatives from 13 organisations in Australia, New Zealand, Sweden, Canada, United Kingdom, and United States responded. The organisations had provided training for 0 to 20 years, with one just starting and another training birth doulas and now expanding. Owners and trainers hold an array of qualifications such as academic, medical, non-medical, and life experience. Curricula have usually been developed locally, and not always included pedagogical consideration, a strategic business model, nor mapping processes such as gap analysis. The organisations are run similarly, and curricula have several consistent topics but with distinctly different approaches. Trainers' views are also mixed about the way to proceed with registration of the Death Doula role. Conclusion: The contrasting views of training organisations explain much of the ambiguity of Death Doulas themselves regarding standardisation of registration, education and role enactment. If heading towards the ultimate goal of professionalisation of the role then a challenging path lies ahead with little in the way of agreement in what this would require.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".