Can fundamental care be advanced using the science of care framework?
Bibliographic record
Abstract
AIMS: This manuscript aims to provide a description of an evidence-informed Science of Care practice-based research and innovation framework that may serve as a guiding framework to generate new discoveries and knowledge around fundamental care in a more integrated manner. BACKGROUND: New ways of thinking about models of care and implementation strategies in transdisciplinary teams are required to accelerate inquiry and embed new knowledge and innovation into practice settings. A new way of thinking starts with an explicit articulation and commitment to the core business of the healthcare industry which is to provide quality fundamental care. DESIGN: This discursive paper delineates an iteratively derived Science of Care research and innovation framework (Science of Care Framework) that draws from a targeted literature review. METHOD: The Science of Care Framework integrates caring science with safety and symptom sciences with implementation, improvement, innovation and team sciences. Each science dimension is described in terms of seminal and evolving evidence and theoretical explanations, focusing on how these disciplines can support fundamental care. CONCLUSIONS: The Science of Care Framework can serve as a catalyst to guide future efforts to propel new knowledge and discoveries around fundamental care and how best to implement it into clinical practice through a transdisciplinary lens. IMPACT ON NURSING SCIENCE, PRACTICE, OR DISCIPLINARY KNOWLEDGE: The Science of Care Framework can accelerate nursing discipline-specific knowledge generation alongside inter and transdisciplinary insights. The novel articulation of the Science of Care Framework can be used to guide further inquiries that are co-designed, and led, by nurses into integrated models of care and innovations in clinical practice.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".