MétaCan
Menu
Back to cohort
Record W109672779 · doi:10.1177/082585970502100402

Physicians’ and Pharmacists’ Attitudes toward the use of Sedation at the End of Life: Influence of Prognosis and Type of Suffering

2005· article· en· W109672779 on OpenAlexaffabout
Danielle Blondeau, Louis Roy, Serge Dumont, Gaston Godin, Isabelle Martineau

Bibliographic record

VenueJournal of Palliative Care · 2005
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsCentre hospitalier universitaire de QuébecUniversité Laval
Fundersnot available
KeywordsExistentialismSedationPalliative sedationMedicinePalliative carePsychotherapistEnd-of-life careHealth carePsychologyHealth professionalsNursingFamily medicinePsychiatryAnesthesia

Abstract

fetched live from OpenAlex

End-of-life sedation remains a controversial and ill-defined clinical practice; its applications vary considerably. With this in mind, a study was conducted using a 2 x 2 experimental design. The variables experimented with were prognosis (short- or long-term) and type of suffering (physical or existential). The goal was to study the influence of the two independent variables on attitude toward sedation. Four clinical vignettes were completed by 124 clinicians, doctors, and pharmacists working in different palliative care environments in the Province of Quebec. The results indicate that the type of suffering influences a subject's attitude to end-of-life sedation. Thus, when a patient was suffering physically, the respondents were significantly in favour of sedation, whereas they were not in favour of this practice if the suffering was existential. Lastly, it is clear that health professionals are uncomfortable when confronted with their patients' existential suffering. This is an issue worth exploring in future studies.

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)0.005
metaresearch head score (Gemma)0.022
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.228
GPT teacher head0.435
Teacher spread0.207 · 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
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Palliative CareSame topicPalliative Care and End-of-Life IssuesFrench-language works237,207