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Record W2118655482 · doi:10.4103/0973-1075.53485

Communication with relatives and collusion in palliative care: A cross-cultural perspective

2009· article· en· W2118655482 on OpenAlexaff
Santosh K. Chaturvedi, Carmen G. Loiselle, Prabha S. Chandra

Bibliographic record

VenueIndian Journal of Palliative Care · 2009
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsMcGill University
Fundersnot available
KeywordsCollusionPerspective (graphical)Palliative careMedicineFace (sociological concept)NursingFamily medicineSociologyBusinessSocial science

Abstract

fetched live from OpenAlex

Handling collusion among patients and family members is one of the biggest challenges that palliative care professionals face across cultures. Communication with patients and relatives can be complex particularly in filial cultures where families play an important role in illness management and treatment decision-making. Collusion comes in different forms and intensity and is often not absolute. Some illness-related issues may be discussed with the patient, whereas others are left unspoken. Particularly in palliative care, the transition from curative to palliative treatment and discussion of death and dying are often topics involving collusion. Communication patterns may also be influenced by age, gender, age, and family role. This paper outlines different types of collusion and how collusion manifests in Indian and Western cultures. In addition, promising avenues for future research are presented.

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)0.008
metaresearch head score (Gemma)0.013
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 score0.011
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0110.013
Scholarly communication0.0070.008
Open science0.0010.006
Research integrity0.0030.004
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 head0.065
GPT teacher head0.433
Teacher spread0.368 · 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

Citations115
Published2009
Admission routes1
Has abstractyes

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