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Record W7046469365

Cross-Cultural Dynamics in Palliative Care: The Emerging\nCanadian Scenario

2016· article· en· W7046469365 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationPalliative careLeverage (statistics)Health careLife expectancyPsychological interventionRelevance (law)PopulationPopulation ageing
DOInot available

Abstract

fetched live from OpenAlex

As modern technologies leverage medical sciences, life expectancy is on the rise in Canada, and indeed globally with a remarkable increase in the elderly population in need of health care. The same is true of the diversity of cultural groups who are now patrons and stakeholders in Canada's health care landscape. An emergent feature ofthis landscape is the complexity ofcontexts for negotiating and mediating medical care delivery at the end of life. This paper examines the gaps in regulatory and legal interventions as well as the gaps and opportunities to negotiate the transition to palliative care in cross-cultural contexts that have the potential to escalate as Canada's domestic health care system increasingly engages with non-dominant segments of Canada's cultural mosaic at the endof-life spectrum. It calls attention to the increased relevance of palliative care, identifying cross-cultural elements required for continuing and future elaboration of that care regime fully into the Canadian health care system.

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)0.007
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.179
Threshold uncertainty score0.577

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0330.043
Scholarly communication0.0100.006
Open science0.0020.014
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0060.000

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 head0.013
GPT teacher head0.288
Teacher spread0.275 · 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 designNot applicable
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

Citations0
Published2016
Admission routes1
Has abstractyes

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