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Record W2997393538 · doi:10.1111/cfs.12725

Parenting challenges of African immigrants in Alberta, Canada

2020· article· en· W2997393538 on OpenAlexafffundabout
Bukola Salami, Dominic A. Alaazi, Philomina Okeke‐Ihejirika, Sophie Yohani, Helen Vallianatos, Brittany Tetreault, Christina Nsaliwa

Bibliographic record

VenueChild & Family Social Work · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsAdministrative Sciences Association of CanadaWomen and Children’s Health Research InstituteUniversity of Alberta
FundersM.S.I. Foundation
KeywordsImmigrationThematic analysisSocioeconomic statusNonprobability samplingMental healthQualitative researchSociologyPsychologyPolitical scienceSocial sciencePopulationDemographyPsychiatry

Abstract

fetched live from OpenAlex

Abstract African immigrant children and youth have some of the poorest social and mental health outcomes in Canada. Although parenting challenges have been widely documented as a key driver of these outcomes, limited systematic research has investigated this phenomenon. In this paper, we report the results of a study examining parenting challenges among a sample of African immigrant parents in Alberta, Canada. We relied on the theoretical lens of transnationalism to collect and analyse data from a purposive sample of African community leaders (n = 14), African immigrant parents (n = 32), and a range of stakeholders (n = 30). Our thematic data analysis revealed several intricately intertwined parenting challenges, organized around six overarching themes, namely, cultural incompatibility, family tension, state interference, limited social supports, poor access to services, and low socioeconomic status. We present these themes and the policy and service implications of our findings.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0130.002
Scholarly communication0.0020.000
Open science0.0010.003
Research integrity0.0000.001
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 head0.021
GPT teacher head0.245
Teacher spread0.224 · 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

Citations31
Published2020
Admission routes3
Has abstractyes

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