Improving Patient Care Through Collaborative Re-envisioning of the Model of Critical Care
Bibliographic record
Abstract
PURPOSE: Advanced medical therapies enable people to live longer with multiple comorbidities, increasing the acuity and complexity of care required upon admission into the intensive care unit. This, coupled with the shift toward a less experienced nursing workforce as skilled nurses retire or leave the critical care environment, prompted the need to re-envision how critical care is delivered. The purpose of this quality improvement project was to embed the values of staff and patient partners in a re-envisioned critical care model, which aligns the provision of high-quality care with staff skill mix and patient needs. METHODS: Through a series of clinical nurse specialist-led engagement sessions with patients, families, and the members of the healthcare team who work in this environment every day, we talked about what is important in critical care. We used their words and thoughts to inform and shape a new sustainable model of critical care. OUTCOME: The 4 components identified as essential to achieving this vision include an acuity-complexity-based approach to care, a transition from separate high-acuity and intensive care areas to integrated units, development of a career pathway to advance practice, and exploration of opportunities to optimize the scope of the interprofessional team. CONCLUSION: Embedding the values and experience of the people directly connected with providing and/or experiencing critical care increases the likelihood of success in developing, implementing, and sustaining a new model of care. This multifaceted approach resulted in 4 distinct components with achievable outcomes that support this re-envisioned patient-centered sustainable model of critical care.
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 | 0.044 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".