Know Your Client and Know Your Team: A Complexity Inspired Approach to Understanding Safe Transitions in Care
Bibliographic record
Abstract
Background. Transitions in care are one of the most important and challenging client safety issues in healthcare. This project was undertaken to gain insight into the practice setting realities for nurses and other health care providers as they manage increasingly complex care transitions across multiple settings. Methods. The Appreciative Inquiry approach was used to guide interviews with sixty-six healthcare providers from a variety of practice settings. Data was collected on participants' experience of exceptional care transitions and opportunities for improving care transitions. Results. Nurses and other healthcare providers need to know three things to ensure safe care transitions: (1) know your client; (2) know your team on both sides of the transfer; and (3) know the resources your client needs and how to get them. Three themes describe successful care transitions, including flexible structures; independence and teamwork; and client and provider focus. Conclusion. Nurses often operate at the margins of acceptable performance, and flexibility with regulation and standards is often required in complex sociotechnical work like care transitions. Priority needs to be given to creating conditions where nurses and other healthcare providers are free to creatively engage and respond in ways that will optimize safe care transitions.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.015 | 0.040 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.008 |
| 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".