MétaCan
Menu
Back to cohort
Record W6921668943 · doi:10.7916/gas3-nh72

Post Migration Mental Health Impacts of Drag Culture Participation among LGBTQ2I+ refugees in Canada

2023· article· en· W6921668943 on OpenAlexaboutno aff

Bibliographic record

VenueColumbia Academic Commons (Columbia University) · 2023
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeMental healthImmigrationPoliticsPublic health

Abstract

fetched live from OpenAlex

Using Intersectional, Minority Stress, and Grounded theoretical principles, and a qualitative interview and quantitative survey approach, this studies examines the mental health impacts of participation in drag culture on LGBTQ2I+ refugees and forced migrants living in Canada. The findings demonstrate that LGBTQ2I+ refugees in Canada who interact with the drag community have increased positive mental health outcomes as a result of reduced avoidance of LGBTQ2I+ activities; increased exploration and performance of gender, sexual orientation; access to resources; sense of community; and improved confidence and sense of freedom. The results also demonstrated that more governmental and institutional supports are needed for LGBTQ2I+ immigrants in Canada to better navigate the immigration system and access education, jobs, and housing, in addition to an expansion of drag culture styles and ethnic, linguistic, and socioeconomic diversity in order to better welcome and accommodate queer individuals from other nations. Ultimately, this study aimed to bring greater insight into the mental health impacts of drag participation for LGBTQ2I+ refugees in Canada.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.299

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0120.002
Scholarly communication0.0040.001
Open science0.0020.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.016
GPT teacher head0.283
Teacher spread0.268 · 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

Citations0
Published2023
Admission routes1
Has abstractno

Explore more

Same venueColumbia Academic Commons (Columbia University)Same topicMigration, Health and TraumaFrench-language works237,207