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Record W4224233186 · doi:10.1080/07481187.2022.2063456

Neuro-oncology clinicians’ perspectives on factors affecting brain cancer patients’ access to medical assistance in dying: A qualitative study

2022· article· en· W4224233186 on OpenAlexaff
Caroline Variath, Seth Climans, Kim Edelstein, Jennifer Bell

Bibliographic record

VenueDeath Studies · 2022
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsQualitative researchDamagesCognitionMedicineCancerMedical decision makingPsychologyOncologyFamily medicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

In most jurisdictions where medical assistance in dying (MAiD) is legal, patients must have decision-making capacity. Brain cancer often damages the cognitive networks required to maintain decision-making capacity. Using qualitative methodology guided by a relational ethics conceptual framework, this study explored neuro-oncology clinicians' perspectives on access to and eligibility for MAiD for patients diagnosed with brain cancer. We interviewed 24 neuro-oncology clinicians from 6 countries. Participants described the unique challenges facing brain cancer patients, potentially resulting in their inequitable access to MAiD. The findings highlight the importance of early end-of-life conversations, advance care planning, and access to end-of-life treatment options.

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)0.003
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.251
GPT teacher head0.585
Teacher spread0.334 · 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.

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

Citations3
Published2022
Admission routes1
Has abstractyes

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