Death doula working practices and models of care: the views of death doula training organisations
Bibliographic record
Abstract
BACKGROUND: The role of death doula has emerged in recent years, arguably as a result of overwhelming demands on carers, healthcare professionals and service providers in end-of-life care. Death doulas work independently without governing oversight and enact the role in various ways. The main driver of this evolving role is the organisations that train them. The aim of this study was to examine death doula training organisations' views with regard to DD business models, incorporating the death doula role into other existing models of care, and role enactment. METHODS: An electronic survey was administered to 15 death doula training organisations in 5 countries asking additionally that they disseminate the survey. Responses were received from 13 organisations, based in Australia (n = 4), the US (n = 4), Canada (n = 2), the UK (n = 1), Sweden (n = 1) and New Zealand (n = 1). This paper provides the qualitative findings from four open-text questions posed within the survey related to models of care. RESULTS: Qualitative data analysis was inductive, themes were determined in relation to: (1) standardised business model for death doulas, (2) death doulas incorporated into existing models of care or existing funding options, (3) death doulas who volunteer their services rather than charge money, and (4) role specialisation such as has occurred with birth doulas. CONCLUSIONS: The death doula role has the potential to be formally recognised in the future under national registration schemes, accompanied by death doula training required via certification. Until such time the death doula role will continue to evolve much as the birth doula role has, organically and unstructured. How and if death doulas are incorporated into existing models of health or social care remains to be seen as the organisations that train them push for independence, flexibility and fiscal independence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".