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Record W4308387708 · doi:10.1177/13634615221135423

No time to grieve: Inuit loss experiences and grief practices in Nunavik, Quebec

2022· article· en· W4308387708 on OpenAlexafffundabout
Shawn Renée Hordyk, Mary Ellen Macdonald, Paul Brassard, Looee Okalik, Louisa Papigatuk

Bibliographic record

VenueTranscultural Psychiatry · 2022
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsMcGill University Health CentreMcGill UniversityUniversité du Québec à Montréal
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsGriefGrassrootsDisenfranchised griefPsychologyPolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

This article presents an overview of past and current grief rituals and practices and existing grassroots and institutional initiatives seeking to address the complex, prolonged, and traumatic grief experienced by many Inuit living in Quebec. While conducting a study seeking to identify the strengths, resources, and challenges for Nunavik's Inuit communities related to end-of-life care, results emerged concerning how family caregivers' grief related to the dying process was compounded by the sequelae of historic loss experiences (e.g., losses related to Canada's federal policies, including residential schools, forced relocations, and dog slaughters) and by present loss experiences (e.g., tragic and sudden deaths in local communities). To better support caregivers, an understanding of these grief experiences and a vision of bereavement care inclusive of community mobilization efforts to develop bereavement training and support is needed. We conclude with a discussion of a community capacity approach to bereavement care.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0200.005
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.337
Teacher spread0.315 · 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 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

Citations4
Published2022
Admission routes3
Has abstractyes

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