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Record W4402619786 · doi:10.1111/nin.12674

It's all about relationships: Developing nurse‐led primary health care in rural communities

2024· article· en· W4402619786 on OpenAlexaff
Sue Randall, Debra Jones, Giti Hadaddan, Danielle White, Rochelle Einboden

Bibliographic record

VenueNursing Inquiry · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
FundersUniversity of Sydney
KeywordsPrimary health carePrimary careNursingPsychologyMedicineFamily medicineEnvironmental health

Abstract

fetched live from OpenAlex

The role of nurses in leading the design and delivery of primary health care services to address health inequities is growing in prominence, specifically in rural Australia. However, limited evidence exists to inform nurse-led primary health care in this context. Based on a focus group with nursing executives and semi-structured interviews with registered nurses we describe nurse experiences of leading the design of a primary health care service in rural Australia and nurse transition to and practice in this service. Nurse experiences were analysed using reflexive thematic analysis. The study reveals the centrality of relational integration in service design and nurse acquisition of relational practice as it relates to nurse to care recipient and nurse to nurse relationships. Tensions between primary health care nurses and their peers, and resultant de-valuing of primary health care practice, are described. The acquisition of nurse professional agency draws attention to investments required to position nurses to lead and sustain care innovations external to hospital settings. The authors propose that relational approaches may provide nurses with the opportunity to reframe their leadership and service contributions towards community literate primary health care provision and provide a pathway to professional emancipation from constrained practice expectations.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.135
GPT teacher head0.484
Teacher spread0.349 · 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.

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
Published2024
Admission routes1
Has abstractyes

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