It's all about relationships: Developing nurse‐led primary health care in rural communities
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".