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Record W1976272915 · doi:10.1191/0269216305pm993oa

Physician-assisted death: attitudes and practices of community pharmacists in East Flanders, Belgium

2005· article· en· W1976272915 on OpenAlexaboutno aff
Johan Bilsen, J. Bernheim, Robert Vander Stichele, Luc Deliëns

Bibliographic record

VenuePalliative Medicine · 2005
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLegislationFamily medicineMedical prescriptionQuarter (Canadian coin)Physician assisted suicideAssisted suicideNursingPsychiatryLaw

Abstract

fetched live from OpenAlex

This study investigates attitudes and practices of community pharmacists with respect to physician-assisted death. Between 15 February and 15 April 2002, we sent anonymous mail questionnaires to 660 community pharmacists in the eastern province of Flanders, Belgium. The response rate was 54% (n = 359). Most of the pharmacists who responded felt that patients have the right to end their own life (73%), and that under certain conditions physicians may assist the patient in dying (euthanasia: 84%; physician-assisted suicide: 61%). Under the prevailing restrictive legislation, a quarter of the pharmacists were willing to dispense lethal drugs for euthanasia versus 86% if it were legalized, but only after being well informed by the physician. The respondents-favour guidelines for pharmacists drafted by their own professional organizations (95%), and enforced by legislation (90%) to ensure careful end-of-life practice. Over the last two years, 7.3% of the responding pharmacists have received a medical prescription for lethal drugs and 6.4% have actually dispensed them. So we can conclude that community pharmacists in East Flanders were not adverse to physician-assisted death, but their cooperation in dispensing lethal drugs was conditional on clinical information about the specific case and on protection by laws and professional guidelines.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.220
GPT teacher head0.512
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

Citations14
Published2005
Admission routes1
Has abstractyes

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