Transforming Home Community Nursing Practice Through Nurse Empowerment: H.O.P.E. Approach to Care
Bibliographic record
Abstract
Ageing at home is the expressed wish of the fastest growing older adult population. Life care, not just medical/health care, can holistically address care needs of this population. Receiving care at home provides the best opportunity for care providers to address a person’s care needs within their natural environment- the place they call home. With heightened concerns of COVID-19 transmission, older audits are hesitant to have multiple care providers in the home and staff are overwhelmed with work as several team members are off due to personal and/or medical reasons. Coupled with the current state of the Canadian homecare practice setting at the system level, a change is needed. The pandemic became the catalyst to transform/evolve the way SE Health provides care for the betterment of our clients, staff and organization- thus spearheading the H.O.P.E.- Home, Opportunity, People, Empowerment model of care. This leading organizational model of care provides nurses with optimal autonomy and flexibility to deliver best life, home and community care. Empowerment provides nurses with optimal autonomy and flexibility to deliver excellent life care with clients and their circle of care members. Caseload management and self-directed scheduling are unique home and community care competencies that are fostered and supported by optimization of operational processes. Therefore, operationalization of nursing practice in this model requires both optimization of Service Coordination function and digital enablement. One digital innovation, MySE Life App was created to address this need. The MySE Life App is a bespoke digital application designed by nurses, for nurses that provides access to key information to support holistic care delivery in a single application. The tool empowers informed decision making around planning, coordination, and delivering exceptional quality care by giving nurses access to client information on demand. The components of H.O.P.E. Model of Care brings homecare back to basics and care back to the future. To bring this ambition to life, we employed implementation science to bring rigor to the implementation approach and support the change management of over 8,000 leaders across the organization nationally. As Primary Nursing is the core of this organizational model, first year of implementation has focused on optimizing scope of nursing practice and continuity of care. H.O.P.E. nursing competency development through a Novice to Expert journey was also undertaken. Three key success insights will be shared with real life implementation scenarios: 1) empowering the nurse and the client, 2) implementation science informed execution, and 3) operationalizing nurse practice with enabling technology and redesigned structures, processes, and leadership. Learning insights can inform healthcare organizations in how creating a nurse empowered practice culture impacts client safety, quality care and sustainable business outcomes. As the implementation moves towards sustainability in nursing, the learnings will also be applied to accelerate the implementation across other health care disciplines such as personal support workers and allied health disciplines in the organization. The implementation of H.O.P.E. Model of Care illustrates how home and community care can work better for people and the broader system in a resource constrained environment.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".