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Record W4310573560 · doi:10.1177/08404704221136847

Support structures for healthcare professionals involved in medical assistance in dying: Quebec, Canada, and the international landscape

2022· article· en· W4310573560 on OpenAlexafffundabout
Catherine Perron, Marie-Éve Bouthillier, Éric Racine

Bibliographic record

VenueHealthcare Management Forum · 2022
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsMcGill UniversityMontreal Clinical Research InstituteUniversité de MontréalCentre Integre de Sante et de Services Sociaux de Laval
FundersUniversité de MontréalMinistère de la Santé et des Services sociaux
KeywordsMandateConstitutionScope (computer science)Service (business)Health carePopulationPublic relationsPolitical scienceSociologyLawBusiness

Abstract

fetched live from OpenAlex

When the Act Respecting End-of-Life Care came into effect in Quebec in 2015, nearly 30 Interdisciplinary Support Groups (ISGs) were formed to accompany practitioners and managers in the clinical, administrative, legal, and ethical practice of Medical Assistance in Dying (MAiD). Today, significant variability is observed in the constitution, role and functioning of ISGs. Based on an overview of national and international support structures, we highlight the strengths and challenges of ISGs. This article presents the results of the first phase of research conducted with 245 people involved in the practice of MAiD in Quebec. The objective is to survey current ISG practices in order to contrast them with those of equivalent structures in Canada and around the world. The intention is to guide leaders in the development of support structures for their institutions. In summary, ISGs are distinguished by their interdisciplinary constitution, their decentralized nature, and their proximity to the teams in the field. However, their service offer remains largely unknown to caregivers and the general population. This can be explained by the undefined and unlimited nature of their mandate, but also by the gap between the scope of their mandate and the lack of funding they receive.

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)0.000
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: none
Teacher disagreement score0.633
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.051
GPT teacher head0.394
Teacher spread0.342 · 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

Citations11
Published2022
Admission routes3
Has abstractyes

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