MétaCan
Menu
Back to cohort
Record W3197830852 · doi:10.3390/nursrep11030062

Empowering Public Health Nurses and Community Home Visitors through Effective Communication Relationships

2021· article· en· W3197830852 on OpenAlexaffabout
Debbie Sheppard-LeMoine, Megan Aston, Lisa Goldberg, Judy E. MacDonald, Deb Tamlyn

Bibliographic record

VenueNursing Reports · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsDalhousie UniversityUniversity of Windsor
Fundersnot available
KeywordsNova scotiaFocus groupQualitative researchSociologyNursingPublic healthPublic health nursingPopulationDiscourse analysisPublic relationsMedicinePolitical scienceSocial science

Abstract

fetched live from OpenAlex

Home visiting programs for marginalized families have included both Public Health Nurses (PHNs) and Community Home Visitors (CHV). Support for families requires health care providers to implement effective communication and collaboration practices; however, few studies have examined how this is carried out. The purpose of this qualitative research study was to explore how an Enhanced Home Visiting (EHV) program in Nova Scotia Canada was organized, delivered through the experiences of PHNs and CHVs. Feminist post-structuralism informed by discourse analysis was used to understand how their experiences were socially and institutionally constructed. Individual semi-structured interviews were conducted with 6 PHNs and 8 CHVs and one focus group was held with 10 of the participants. A social discourse on mothering layered within a social discourse of working with a vulnerable population added a deeper understanding of how communication was constructed through the everyday practices of PHNs and CHVs. Findings may be used to inform reporting and communication practices between health care providers who work with marginalized families.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0080.007
Scholarly communication0.0050.002
Open science0.0010.008
Research integrity0.0010.001
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 head0.120
GPT teacher head0.493
Teacher spread0.373 · 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
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueNursing ReportsSame topicHealth, psychology, and well-beingFrench-language works237,207