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Record W4210556960 · doi:10.15273/dmj.vol48no1.11262

Uptake in the practice of medical assistance in dying (MAiD) and involvement by physician speciality over time in Nova Scotia, Canada

2022· article· en· W4210556960 on OpenAlex
Elizabeth Munn, Emily Gard Marshall

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueDalhousie Medical Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsDalhousie University
FundersDepartment of Health, Western Cape GovernmentNova Scotia Department of Health and Wellness
KeywordsNova scotiaSpecialtyDemographicsLegislationFamily medicineMedicineGerontologyPolitical scienceDemographyHistorySociologyEthnologyLaw

Abstract

fetched live from OpenAlex

Legislation on medical assistance in dying (MAiD) was enacted in Canada in 2016. There is limited research on the topic available from Atlantic Canada. This study provides early data on the uptake of MAiD in Nova Scotia based on analysis of administrative billing data. It presents the number of MAiD cases by year from 2017 through early 2020. It also provides data on physician involvement in the MAiD process by specialty, broken down by assessors and providers of MAiD. Our data agrees with provincial- and national-level data that family physicians are highly involved in the MAiD process. Our study also documents physician involvement in conducting MAiD assessments by specialty, a metric which is not widely available in the literature. This study emphasizes the need for robust, provincial-level data on the demographics of providers involved in MAiD.

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.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.380
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.366
Teacher spread0.322 · 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