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Record W4226034398 · doi:10.1186/s12913-022-07662-7

Effectiveness of registered nurses on system outcomes in primary care: a systematic review

2022· review· en· W4226034398 on OpenAlexaff
Julia Lukewich, Shabnam Asghari, Emily Gard Marshall, Maria Mathews, Michelle Swab, Joan Tranmer, Denise Bryant‐Lukosius, Ruth Martin‐Misener, Allison A. Norful, Dana Ryan, Marie-Ève Poitras

Bibliographic record

VenueBMC Health Services Research · 2022
Typereview
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsUniversité de SherbrookeQueen's UniversityMcMaster UniversityWestern UniversityDalhousie UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsCINAHLMedicinePsycINFOMEDLINEPsychological interventionNursing researchHealth informaticsContext (archaeology)Health administrationHealth careSystematic reviewNursingWorkloadFamily medicineHealth services researchPublic health

Abstract

fetched live from OpenAlex

BACKGROUND: Internationally, policy-makers and health administrators are seeking evidence to inform further integration and optimal utilization of registered nurses (RNs) within primary care teams. Although existing literature provides some information regarding RN contributions, further evidence on the impact of RNs towards quality and cost of care is necessary to demonstrate the contribution of this role on health system outcomes. In this study we synthesize international evidence on the effectiveness of RNs on care delivery and system-level outcomes in primary care. METHODS: A systematic review was conducted in accordance with Joanna Briggs Institute methodology. Searches were conducted in CINAHL, MEDLINE Complete, PsycINFO, and Embase for published literature and ProQuest Dissertations and Theses and MedNar for unpublished literature between 2019 and 2022 using relevant subject headings and keywords. Additional literature was identified through Google Scholar, websites, and reference lists of included articles. Studies were included if they measured effectiveness of a RN-led intervention (i.e., any care/activity performed by a primary care RN within the context of an independent or interdependent role) and reported outcomes of these interventions. Included studies were published in English; no date or location restrictions were applied. Risk of bias was assessed using the Integrated Quality Criteria for Review of Multiple Study Designs tool. Due to the heterogeneity of included studies, a narrative synthesis was undertaken. RESULTS: Seventeen articles were eligible for inclusion, with 11 examining system outcomes (e.g., cost, workload) and 15 reporting on outcomes related to care delivery (e.g., illness management, quality of smoking cessation support). The studies suggest that RN-led care may have an impact on outcomes, specifically in relation to the provision of medication management, patient triage, chronic disease management, sexual health, routine preventative care, health promotion/education, and self-management interventions (e.g. smoking cessation support). CONCLUSIONS: The findings suggest that primary care RNs impact the delivery of quality primary care, and that RN-led care may complement and potentially enhance primary care delivered by other primary care providers. Ongoing evaluation in this area is important to further refine nursing scope of practice policy, determine the impact of RN-led care on outcomes, and inform improvements to primary care infrastructure and systems management to meet care needs. PROTOCOL REGISTRATION ID: PROSPERO: International prospective register of systematic reviews. 2018. ID= CRD42018090767 .

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)0.029
metaresearch head score (Gemma)0.098
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.029
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.098
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0100.009
Bibliometrics0.0070.009
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.002
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 head0.244
GPT teacher head0.591
Teacher spread0.348 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations68
Published2022
Admission routes1
Has abstractyes

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