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Record W179295932 · doi:10.1177/082585970902500103

The “Good” Rural Death: A report of An Ethnographic Study in Alberta, Canada

2009· article· en· W179295932 on OpenAlexaffabout
Donna M. Wilson, Lise Fillion, Roger E. Thomas, Christopher Justice, Paramjit P. Bhardwaj, Anne‐Marie Veillette

Bibliographic record

VenueJournal of Palliative Care · 2009
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsHôtel-Dieu de QuébecUniversity of TorontoUniversité LavalUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsViewpointsEthnographyPalliative careRural areaFocus groupGood deathEnd-of-life careQualitative researchHospice careNursingMedicineEconomic growthSocioeconomicsGeographySociologyPolitical scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

Much concern has centred on the "good" death since the modern hospice/palliative care movement began, and considerable progress has been made in urban services to promote the good death. Little is known about the perspectives of people who live in rural and remote areas of Canada on the good death and how this good death might be enabled in those areas. This report is of an ethnographic study in rural Alberta involving English-speaking Albertans. An identical study in Quebec will be reported elsewhere. The 2006-07 Alberta study involved 13 interviews with individuals to understand their personal viewpoints or perspectives and how they were shaped by their experiences, followed by focus group discussions in two representative rural communities for additional insights from rural policy-makers and care providers. Four themes in the Alberta data highlight critical elements of the good rural death. These findings are expected to contribute to rural/remote palliative and end-of-life care developments.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.415

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.007
Science and technology studies0.0300.009
Scholarly communication0.0050.001
Open science0.0030.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.082
GPT teacher head0.418
Teacher spread0.336 · 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 source (direct Gemma or distilled Codex), 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

Citations65
Published2009
Admission routes2
Has abstractyes

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