Bibliographic record
Abstract
Our country's recent negotiations for a new Health Accord have shone light on the importance of more accessible and better home care. The direction being taken on health funding investments has sent a strong message about healthcare system redesign. It is time to design a healthcare system that moves us away from a hospital-focused model to one that is more effective, integrated and sustainable and one that places a greater emphasis on primary care, community care and home care. The authors of the lead paper (Sharkey and Lefebre 2017) provide their vision for people-powered care and explore the opportunity for nursing leaders to draw upon the unique expertise and insights of home care nursing as a strategic lever to bring about real health system transformation across all settings. Understanding what really matters at the beginning of the healthcare journey and honouring the tenants of partnership and empowerment as a universal starting point to optimize health outcomes along the continuum of care present a very important opportunity. However, as nursing leaders in the health system change, it is important that we extend the conversation beyond one setting. It is essential that as leaders, we seek to design models of care delivery that achieve a shared vision, focused on seamless coordinated care across the continuum that is person-centred. Bringing about real system change requires us to think differently and consider the role of nursing across all settings, collaboratively co-designing so that our collective skills and knowledge can work within a complementary framework. Focusing our leadership efforts on enhancing integration across healthcare settings will ensure that nurses can be important leaders and active decision-makers in health system change. A shared vision for healthcare requires all of us to look beyond the usual practices and structures, hospitals and institutional walls.
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 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 | 0.027 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.058 |
| Scholarly communication | 0.026 | 0.020 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.012 | 0.025 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".