Applying the Synergy Model to inform the nursing model of care in an inpatient and an ambulatory care setting: The experience of two urban cancer institutions, Hamilton Health Sciences and Grand River Regional Cancer Centre
Bibliographic record
Abstract
The incidence and prevalence of cancer continues to rise throughout Canada. Approximately one in two Canadians are expected to develop cancer at some point in their lives (Canadian Cancer Society, 2021). As the complexity and acuity of individuals with cancer increases, there is increased necessity to define the ideal nurse-to-patient ratio and patient caseload for nurses in specialized oncology settings. Two senior nurse leaders, faced with the need to determine the most appropriate model to inform the nursing model of care within their respective care areas, collaborated and decided to implement the Synergy Model. The Synergy Model is a professional practice model developed by the American Association of Critical Care Nurses (AACN). In the Synergy Model, nursing care reflects the integration of nurses' knowledge, skills, attitudes, competencies, and experience to meet the needs of patients and families (Curley, 2007). This model provides a framework for matching nursing resources based on patient care needs and has been adapted in various care settings. The model, however, has not been applied in a surgical oncology inpatient unit or in an oncology ambulatory care setting. Using a quality improvement methodology, the Synergy Model was piloted in these new areas and found to be effective. The Synergy Model can be utilized to determine the need for additional nursing resources with specialized oncology nurses and appropriate skill mix of intraprofessional nursing teams. It can also be used to assess adult oncology patients who present to the ambulatory systemic care suite for unscheduled care related to symptomatic concerns.
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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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".