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Record W4328023695 · doi:10.7202/1097405ar

L’aide médicale à mourir a-t-elle protégé la santé mentale des personnes endeuillées pendant la pandémie de COVID-19 ?

2023· article· fr· W4328023695 on OpenAlexaffabout
Jacques Cherblanc, Isabelle Côté, Susan Cadell, Chantal Verdon, Josée Grenier, Chantale Simard, David Wright, Christiane Bergeron‐Leclerc

Bibliographic record

VenueIntervention · 2023
Typearticle
Languagefr
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsUniversity of OttawaUniversité du Québec en OutaouaisUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsHumanitiesCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)ArtMedicineVirology

Abstract

fetched live from OpenAlex

L’aide médicale à mourir (AMM) est légalement permise depuis 2015 au Québec et depuis 2016 ailleurs au Canada. Même dans les régions où la mort assistée est pratiquée depuis des décennies, peu d’attention a été portée aux personnes qui ont perdu un proche dans ces circonstances. On ne sait donc pas exactement quels effets peut avoir l’AMM sur le vécu du deuil des personnes concernées, et encore moins en situation de pandémie. À partir des données quantitatives et qualitatives du projet Covideuil, cet article entend éclairer plus précisément le vécu du deuil à la suite du décès par AMM d’un proche pendant la pandémie de COVID-19 au Canada. Il ressort de ces analyses que l’AMM ne semble pas être associée à une trajectoire de deuil distincte pendant la pandémie. Ce type de décès nécessite cependant que les intervenants sociosanitaires portent une attention particulière aux proches de la personne décédée, car leur accompagnement est moins systématisé que pour les soins palliatifs.

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)0.004
metaresearch head score (Gemma)0.017
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 score0.728
Threshold uncertainty score0.548

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.075
GPT teacher head0.425
Teacher spread0.350 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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