MétaCan
Menu
Back to cohort
Record W2316320077 · doi:10.1097/njh.0b013e3182632e3b

Understanding Death and Dying Among the Low-German-Speaking Mennonites

2013· article· en· W2316320077 on OpenAlexaff
Judith C. Kulig, HaiYan Fan

Bibliographic record

VenueJournal of Hospice and Palliative Nursing · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsCanadian Institutes of Health ResearchUniversity of Lethbridge
Fundersnot available
KeywordsGermanHonorFaithWishQualitative researchRelation (database)PsychologyPopulationSocial psychologyNursingPublic relationsSociologyMedicinePolitical scienceInternet privacyTheologySocial scienceHistory

Abstract

fetched live from OpenAlex

Closed religious groups are a part of our society, but oftentimes there is a limited understanding of their unique beliefs and practices in relation to death and dying. Based on an existing clinical relationship with one such group—the Low-German-speaking Mennonites—a research program has been developed and implemented to address issues noted by health professionals and social service providers who wish to more effectively care for this population but lack an understanding of their beliefs and practices. This article reports on a study on death and dying that was conducted to attend to this knowledge gap and inform clinicians about ways to provide appropriate care for the dying. The qualitative interviews that were conducted with this unique religious group revealed their experiences of death and dying and their practices to honor this transition. Participants believed that suffering is related to the person’s relationship with God, and a slow death will provide time to atone for one’s sins. Providing care that is more closely aligned with these perspectives can be accomplished if providers are willing to approach this group in a respectful manner that allows for open discussion so that decisions based on faith are made.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.059
GPT teacher head0.270
Teacher spread0.210 · 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 designObservational
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

Citations3
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Hospice and Palliative NursingSame topicAgriculture and Farm SafetyFrench-language works237,207