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Record W3129530941 · doi:10.32799/ijih.v16i2.33230

Indigenous End-of-Life Doula Course: Bringing the Culture Home

2021· article· en· W3129530941 on OpenAlexaffvenueabout
Gina Gaspard, Carrie Gadsby, Jennifer Mallmes

Bibliographic record

VenueInternational Journal of Indigenous Health · 2021
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsDouglas College
Fundersnot available
KeywordsIndigenousCounterintuitiveLife course approachSociologyPsychologyGerontologyMedicineSocial psychologyEcology

Abstract

fetched live from OpenAlex


 
 
 Many Indigenous people who live on their traditional territory die in hospital when their preference is to enter the spirit world from their home. Indigenous people in Canada describe experiencing many barriers that prevent them from making this final choice in life. The First Nations Health Authority in British Columbia (BC), Canada, in collaboration with Douglas College, offered end-of- life doula training classes to Indigenous people in BC in 2019. The goal was to build on the strengths of community members already supporting people and their families during their final journey into the spirit world. There were 86 participants (72% identified as Indigenous) from the five health regions in BC, representing 47 Indigenous communities. Participants were overwhelmingly satisfied with the five-day course and planned to take their new learnings back to their community. It was noted, however, that this course would benefit from adaptations, including a greater emphasis on traditional Indigenous practices, facilitation tips, and strategies to support people through loss and bereavement. Furthermore, the term “end-of-life doula” is sometimes associated with a for-profit business, which is counterintuitive to traditional Indigenous practices, highlighting the necessity for a name change. Further evaluation over the next year is necessary to confirm that the course makes a positive difference in the final journey for Indigenous people.
 
 

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.029
GPT teacher head0.380
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2021
Admission routes3
Has abstractyes

Explore more

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