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Record W3004843275 · doi:10.1177/1043659620902812

Immigrant Mothers’ Perspectives of Barriers and Facilitators in Accessing Mental Health Care for Their Children

2020· article· en· W3004843275 on OpenAlexaffabout
Mia Tulli, Bukola Salami, Lule Begashaw, Salima Meherali, Sophie Yohani, Kathleen Hegadoren

Bibliographic record

VenueJournal of Transcultural Nursing · 2020
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMental healthRefugeeLanguage barrierRacismFocus groupFeelingQualitative researchImmigrationService providerNursingCultural competenceInterpreterMedicineStigma (botany)PsychologyService (business)PsychiatrySociologySocial psychologyPolitical scienceGender studies

Abstract

fetched live from OpenAlex

Introduction: Data on immigrant and refugees’ access to services in Canada typically focus on adult populations generally but not children specifically. To fill this gap, this study explored immigrant and refugee mothers’ perceptions of barriers and facilitators for mental health care for their children in Edmonton, Alberta, Canada. Method: In this qualitative descriptive study, researchers conducted 18 semistructured interviews with immigrant and refugee mothers who live in Edmonton, self-identify as women, and have children living in Canada. Results: Barriers included financial strain, lack of information, racism/discrimination, language barriers, stigma, feeling isolated, and feeling unheard by service providers. Facilitators included schools offering services, personal levels of higher education, and free services. Discussion: Nurses can improve access to mental health services by addressing issues related to racism within the health system, by creating awareness related to mental health, and by providing trained interpreters to help bridge barriers in communications.

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)0.003
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 score0.447
Threshold uncertainty score0.889

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0080.003
Scholarly communication0.0020.001
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 head0.017
GPT teacher head0.333
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

Citations55
Published2020
Admission routes2
Has abstractyes

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