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Record W4384024817 · doi:10.1080/01612840.2023.2227267

Health Literacy of Healthcare Providers and Mental Health Needs of Immigrant Perinatal Women in British Columbia: A Critical Ethnography

2023· article· en· W4384024817 on OpenAlexaffabout
Conchitina Lluch, Joyce O’Mahony, Melba Sheila D’Souza, Roula Kteily-Hawa

Bibliographic record

VenueIssues in Mental Health Nursing · 2023
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsWestern UniversityThompson Rivers University
Fundersnot available
KeywordsMental healthHealth literacyHealth careNursingImmigrationChildbirthParticipant observationLiteracyMedicinePsychologyPsychiatrySociologyPregnancyPolitical science

Abstract

fetched live from OpenAlex

AIMS: This research explores how health care providers determine the mental health needs of immigrant women in the perinatal phase of childbirth. The contextual factors that affect the mental health of these women and influence their engagement with the British Columbian communities in which they reside are investigated. METHOD: Using a critical ethnographic approach, eight health care providers were interviewed to gain insight into health care provider's health literacy and immigrant perinatal women's mental health. Each participant was interviewed for 45-60 min in the period from January to February 2021 to obtain relevant data. RESULTS: Three themes emerged from the data analysis: the health care provider's role and his/her health literacy, the health literacy of the participant, and the impact of the ongoing COVID-19 pandemic on the participant's situation. CONCLUSIONS: The findings indicate that a healthy working relationship between the health care provider and an immigrant woman in the perinatal phase of childbirth is essential to facilitate an effective interchange of health information.

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)0.002
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 score0.439
Threshold uncertainty score0.882

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.005
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
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 head0.019
GPT teacher head0.407
Teacher spread0.387 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

Explore more

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