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Record W4232409043 · doi:10.32920/ryerson.14664525

Exploring experiences of grief and loss after suicide of a loved one in South Asian families

2021· preprint· en· W4232409043 on OpenAlexaff
Sanjini Phillips

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsToronto Metropolitan UniversityCentre for Social Innovation
Fundersnot available
KeywordsGriefMainstreamPerspective (graphical)NarrativeQualitative researchPsychologyNegotiationPsychotherapistSociologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

This research explores the experiences of South Asian families who have experienced suicide of a loved one in the past five-years. Past research in the areas of suicide, grief, loss, and bereavement has not focused on how different cultures live through and may view these issues through a cultural lens. The research uses a qualitative narrative methodology and to explore how South Asian families understand and interpret suicide and negotiate their personal experiences of loss and grief after suicide. Individual interviews were conducted and recorded on audiotape. Participants were provided with an opportunity to explore and share their experiences of suicide of a loved one from a cultural perspective, voices that are missing in the mainstream literature. This research contributes to a broader understanding of suicide in diverse communities and how helping professionals can work towards a more inclusive practice with South Asian families who have experiences suicide.

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)0.007
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 score0.010
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0100.008
Scholarly communication0.0040.003
Open science0.0010.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.120
GPT teacher head0.345
Teacher spread0.226 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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