Implementing the Synergy Model: A Qualitative Descriptive Study
Bibliographic record
Abstract
Hospitals across our nation are seeking to implement models of care that meet the primary goals of Quadruple Aim: Improved population health, cost-effective care delivery, and patient and provider satisfaction. In an effort to address the Quadruple Aim and our patients' care needs, Hamilton Health Sciences (HHS) embarked on a model of care delivery redesign, beginning with nursing care delivery. From 2013 to 2018, 12 clinical programs at HHS implemented the Synergy Model with its accompanying synergy patient needs assessment tool for nurses to objectively assess patients' acuity and dependency needs. Data on patients' priority care needs were used to inform a nursing model of care redesign at HHS, including skill mix and staffing levels. This five-year project was an organization-wide quality improvement initiative. As part of the evaluation, HHS leaders partnered with health services nurse researchers to conduct a mixed methods study. This paper describes the evaluation outcomes from the qualitative component of the study, which included interviews with clinical nurse leaders and direct care nurses. Data were analyzed using descriptive thematic analysis. Some key findings were increased nurse awareness of patients' holistic care needs and leaders' capacity to plan staffing assignments based on patients' priority care needs. Themes helped inform recommendations for key stakeholders, including nurse leaders and direct care nurses.
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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 | 0.026 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".