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Record W2036711723 · doi:10.12968/bjcn.2012.17.3.134

Using referral guidelines to support best care outcomes for patients

2012· article· en· W2036711723 on OpenAlexaff
Ben Bowers, Rosemary Cook

Bibliographic record

VenueBritish Journal of Community Nursing · 2012
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsQueen's University
Fundersnot available
KeywordsReferralMedicineDistrict nurseNursingBest practiceMinimum Data SetNurse practitionersSet (abstract data type)Family medicineHealth careNursing homes

Abstract

fetched live from OpenAlex

District nurses play a pivotal role in individuals' care pathways by meeting their needs in the community. However, district nurses are frequently referred patients for whom other interprofessional colleagues have more suitable skills to help in achieving their optimum care outcome. Various major reports have identified a clear need to define what district nurses do and how they will respond appropriately to patients' needs. However, there remains only tacit understanding of district nurse referral criteria across the country and within community organizations. This article discusses how a set of facilitative district nurse referral guidelines have been devised to support individuals in achieving their best care outcome. We also debate approaches to managing referrals to district nursing services and the pressing need to ensure these are effectively managed in practice.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.283
GPT teacher head0.569
Teacher spread0.286 · 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 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

Citations10
Published2012
Admission routes1
Has abstractyes

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