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Record W2955520425 · doi:10.1111/hsc.12797

Optimization of home care nurses in Canada: A scoping review

2019· review· en· W2955520425 on OpenAlexaffabout
Rebecca Ganann, Annette Weeres, Annie Lam, Harjit Chung, Ruta Valaitis

Bibliographic record

VenueHealth & Social Care in the Community · 2019
Typereview
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsRegistered Nurses' Association of OntarioMcMaster University
Fundersnot available
KeywordsStaffingWorkforceSkill mixNursingScope of practicePsychological interventionWorkloadMedicineHealth carePolitical science

Abstract

fetched live from OpenAlex

Nurses are among the largest providers of home care services thus optimisation of this workforce can positively influence client outcomes. This scoping review maps existing Canadian literature on factors influencing the optimisation of home care nurses (HCNs). Arskey and O'Malley's five stages for scoping literature reviews were followed. Populations of interest included Registered Nurses, Registered/Licensed Practical Nurses, Registered Nursing Assistants, Advanced Practice Nurses, Nurse Practitioners and Clinical Nurse Specialists. Interventions included any nurse(s), organisational and system interventions focused on optimising home care nursing. Papers were included if published between January 1, 2002 up to May 15, 2015. The review included 127 papers, including 94 studies, 16 descriptive papers, 6 position papers, 4 discussion papers, 3 policy papers, 2 literature reviews and 2 other. Optimisation factors were categorised under seven domains: Continuity of Care/Care; Staffing Mix and Staffing Levels; Professional Development; Quality Practice Environments; Intra-professional and Inter-professional and Inter-sectoral Collaboration; Enhancing Scope of Practice: and, Appropriate Use of Technology. Fragmentation and underfunding of the home care sector and resultant service cuts negatively impact optimisation. Given the fiscal climate, optimising the existing workforce is essential to support effective and efficient care delivery models. Many factors are inter-related and have synergistic impacts (e.g., recruitment and retention, compensation and benefits, professional development supports, staffing mix and levels, workload management and the use of technology). Quality practice environments facilitate optimal practice by maximixing human resources and supporting workforce stability. Role clarity and leadership supports foster more effective interprofessional team functioning that leverages expertise and enhances patient outcomes. Results inform employers, policy makers and relevant associations regarding barriers and enablers that influence the optimisation of home care nursing in nursing, intra- and inter-professional and inter-organisational contexts.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
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 score0.309
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.004
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 head0.185
GPT teacher head0.511
Teacher spread0.326 · 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 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

Citations79
Published2019
Admission routes2
Has abstractyes

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