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Impact of specialty on attitudes of Australian medical practitioners to end‐of‐life decisions

2008· article· en· W1553216145 on OpenAlexaboutno aff
Malcolm Parker, Colleen Cartwright, Gail Williams

Bibliographic record

VenueThe Medical Journal of Australia · 2008
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
Fundersnot available
KeywordsSpecialtyMedicinePalliative careFamily medicineEnd-of-life careQuarter (Canadian coin)Nursing

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare attitudes and practices of Australian medical practitioners, by specialty, to a range of medical decisions at the end of life. DESIGN, SETTING AND PARTICIPANTS: As part of an international study, in 2003, a structured questionnaire was mailed to 2964 medical practitioners drawn from membership registers of Australian and Australasian professional colleges. Data from 1478 questionnaires were statistically analysed using validated instruments. MAIN OUTCOME MEASURES: Practitioners' willingness to comply with requests from patients and/or their relatives for symptom relief which might also hasten death; provision of terminal sedation and euthanasia, or willingness to provide these on their own initiative. RESULTS: Respondents reported being much more willing to comply with a patient's request for increasing symptom relief, even at risk of hastening death, than for terminal sedation. Over a quarter of respondents would provide terminal sedation to competent patients on their own initiative. A small number of respondents would intentionally hasten death. There were significant differences by specialty for all three actions. Oncologists, palliative care physicians and geriatricians were least likely to actively hasten death, and more likely to act unilaterally to relieve symptoms as a medical necessity. CONCLUSIONS: Perceptions about the causation of death and aspects of medical culture appear to influence physicians' attitudes towards medical decisions at the end of life. Our findings have implications for medical education, interprofessional communication and discussion between the medical profession and the community.

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)0.047
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
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 head0.262
GPT teacher head0.504
Teacher spread0.242 · 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 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

Citations33
Published2008
Admission routes1
Has abstractyes

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