Optimization of home care nurses in Canada: A scoping review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".