MétaCan
Menu
Back to cohort
Record W4323356361 · doi:10.1057/s41599-023-01572-7

Adapting to a new home: resettlement and mental health service experiences of immigrant and refugee youth in Montreal

2023· article· en· W4323356361 on OpenAlexafffundabout
Charles Gyan, Farhin Chowdhury, Ata Senior Yeboah

Bibliographic record

VenueHumanities and Social Sciences Communications · 2023
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcGill University
FundersMcGill University
KeywordsMental healthRefugeeEthnic groupImmigrationService providerService (business)Cultural competencePublic relationsMental health serviceNursingPsychologyPolitical scienceMedicineBusinessPsychiatryPedagogyMarketing

Abstract

fetched live from OpenAlex

Resettlement and mental health services are critical support systems for refugee and immigrant youth (RIY) as they navigate the complexities of settling into their new homes. These services play a vital role in meeting the needs of RIY and helping them feel welcomed into Canadian society. The purpose of this study is to provide insight into RIY's experiences with resettlement and mental health service providers in Montreal, Canada. Adopting a descriptive quantitative research approach, this study utilized online surveys to gather data. The findings indicate that cultural and linguistic barriers are the major obstacles faced by refugee and immigrant youth when accessing resettlement and mental health services in Montreal. Protective resources, such as family, friends, and ethnic communities, were identified as important facilitators of successful integration into Canadian society. To improve services, cultural sensitivity should be a priority for providers, as recommended by this study. By acknowledging the significance of cultural barriers in accessing resettlement and mental health services, this study emphasizes the need for service providers to prioritize cultural sensitivity in their efforts to improve services.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.223
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.175
GPT teacher head0.402
Teacher spread0.227 · 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 teacher head, 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

Citations19
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueHumanities and Social Sciences CommunicationsSame topicMigration, Health and TraumaFrench-language works237,207