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Record W4224433861 · doi:10.1177/10436596221090268

Recruitment Strategies to Engage Newcomer Mothers of African Descent in Maternal Mental Health Research in Canada

2022· article· en· W4224433861 on OpenAlexaffabout
Deborah Baiden, Marilyn Evans

Bibliographic record

VenueJournal of Transcultural Nursing · 2022
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsWestern UniversityUniversity of Toronto
Fundersnot available
KeywordsMental healthPsychological resiliencePsychologyQualitative researchAfrican descentMental illnessMedicinePsychiatrySocial psychologySociology

Abstract

fetched live from OpenAlex

INTRODUCTION: Newcomer mothers of African descent are at risk for maternal mental stress because of inadequate social support, newcomer status, and stress of motherhood. Limited participation of newcomer African mothers in mental health research contributes to a knowledge gap in this area further impacting culturally competent health services. This article reports recruitment strategies to better engage African newcomer women in maternal mental health research. METHODS: In-depth discussion of recruitment strategies, used in a qualitative descriptive study conducted with Black African newcomer mothers in Canada. RESULTS: Ten African newcomer mothers were successfully recruited using recruitment strategies such as engagement with religious organizations, snowballing, and the use of social media. DISCUSSION: Cultural beliefs on motherhood, resilience, and mental illness may account for hesitancy to engage in maternal mental health research. Recruitment strategies could help overcome the challenges and potentially diversify maternal mental health research in Canada through the engagement of African newcomer mothers.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.185
GPT teacher head0.419
Teacher spread0.235 · 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 designObservational
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

Citations4
Published2022
Admission routes2
Has abstractyes

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