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The Cultural Differences in Perceived Value of Disclosure and Cognition: Spain and Canada

2003· article· en· W5908031 on OpenAlexaffabout
Robin L. Fainsinger, Juan Manuel Núñez-Olarte, Donna deMoissac

Bibliographic record

VenueJournal of Palliative Care · 2003
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsRoyal Alexandra HospitalUniversity of Alberta
Fundersnot available
KeywordsSomnolenceCognitionPsychologyAnxietyDistressPopulationPsychiatryClinical psychologyMedicineAdverse effect

Abstract

fetched live from OpenAlex

A previous multicentre international study on sedation at the end of life has detected major differences between Canadian and Spanish patients. This was particularly evident in the need to sedate Spanish patients for psychological/existential distress. This study was designed to explore the hypothesis that marked differences in the value patients and families attach to disclosure and cognition were a factor. The study population included patients referred to two palliative care consulting services based in acute care hospitals in Madrid, Spain (M), and in Edmonton, Canada (E). Questions addressed the issue of clear thinking, pain/nausea-medication-induced somnolence/confusion, anxiety/antidepressant-medication-induced somnolence/confusion, details of diagnosis. One hundred patients were evaluated on each site. Patients and families in E placed a higher value on clear thinking, change in medication causing somnolence/confusion, and wanting full disclosure. Patients and families in E agreed almost 100% of the time, while agreement in M varied from 42% to 67%. These results suggest major differences in the perceived value of clear cognition and disclosure of information between patients and families in E and M. The lack of agreement between patients and families in M is a further significant factor that may complicate communication with patients and families, as well as medical management.

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)0.001
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 score0.087
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.070
GPT teacher head0.362
Teacher spread0.292 · 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

Citations34
Published2003
Admission routes2
Has abstractyes

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