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Record W4399372912 · doi:10.1017/gmh.2024.60.pr2

Recommendation: Research breakdowns: A constructive critique of research practice involving grief, trauma and displaced people — R0/PR2

2024· peer-review· en· W4399372912 on OpenAlexaff
Clare Killikelly, Hannah Comteße, Franziska Lechner‐Meichsner, Johanna Sam, John S. Ogrodniczuk

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGriefConstructivePsychoanalysisPsychotherapistPsychologySociologyEpistemologyComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

Impactful research on refugee mental health is urgently needed. To mitigate the growing refugee crisis, researchers and clinicians seek to better understand the relationship between trauma, grief and post-migration factors with the aim of bringing better awareness, more resources and improved support for these communities and individuals living in host countries. As much as this is our intention, the prevailing research methods, that is, online anonymous questionnaires, used to engage refugees in mental health research are increasingly outdated and lack inclusivity and representation. With this perspective piece, we would like to highlight a growing crisis in global mental health research; the predominance of a Global North-centric approach and methodology. We use our recent research challenges and breakdowns as a learning example and possible opportunity to rebuild our research practice in a more ethical and equitable way.

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)0.129
metaresearch head score (Gemma)0.614
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.871
Threshold uncertainty score0.683

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1290.614
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0050.009
Science and technology studies0.0100.023
Scholarly communication0.0240.033
Open science0.0130.013
Research integrity0.0620.050
Insufficient payload (model declined to judge)0.1070.103

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 head0.155
GPT teacher head0.533
Teacher spread0.378 · 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.

Study designNot applicable
DomainMethods
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

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