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Record W182730888

Family Home Visitors: Increasing Minority Women’s Access to Health Services

2009· article· en· W182730888 on OpenAlexfundaboutno aff
Mechthild Meyer, Alma Estable, Lynne MacLean, Wendy E. Peterson

Bibliographic record

VenueDigital Scholarship - UNLV (University of Nevada Reno) · 2009
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsnot available
FundersGovernment of Ontario
KeywordsOutreachNursingLanguage barrierService providerService (business)Unit (ring theory)MedicinePsychologyBusinessPolitical science
DOInot available

Abstract

fetched live from OpenAlex

The article describes how service access barriers (language, trust) were addressed at different levels (organization, service provider, community) by involving Family Home Visitors to support Nurse Practitioners in providing pre- and postnatal services to linguis­tic minority women in Ontario. The investigators undertook a secondary analysis of 18 semi-structured interviews with health unit informants, Nurse Practitioners, program us­ers, and community leaders, including Family Home Visitors. Health units facilitated col­laboration between two programs aimed at serving mothers with young children, result­ing in both programs using Family Home Visitors. They enhanced minority women’s trust in Nurse Practitioner services by providing interpretation, outreach and support. Family Home Visitors increased Nurse Practitioners’ community knowledge and insights of the family situation. The findings contribute to our understanding of strategies to overcome language and trust barriers and improve access to programs for isolated women from linguistic minority backgrounds. Family Home Visitors’ role has the potential for being expanded and deserves more system support.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.043
GPT teacher head0.356
Teacher spread0.313 · 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 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

Citations0
Published2009
Admission routes2
Has abstractyes

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