Avoidable Care Transitions: A Consensus-Based Definition Using a Delphi Technique
Bibliographic record
Abstract
Background and Objectives: Older adults are at increased risk of frequent transitions between care settings, even though some care transitions are avoidable. The term "avoidable care transitions" is not clearly defined in the research literature. This study aimed to find a consensus-based definition for "avoidable care transitions." Research Design and Methods: This study was conducted as part of the TRANS-SENIOR research network. A 4-round Delphi survey was based on a literature review that identified existing definitions of "avoidable care transitions." Articles in MEDLINE via PubMed and CINAHL were searched. In total 95 references were included, and 106 definitions were identified. Definitions were coded to find themes, resulting in 3 themes with 2 codes for each. Results: In total, 99 experts from 9 countries were invited, and the response rates in Delphi Rounds 1, 2, 3, and 4 were 37.5%, 19.1%, 33.3%, and 23.3%, respectively. Upon reaching the predefined minimum of 90% agreement, the following definition was declared as final: "Avoidable care transitions (a) are without significant patient-relevant benefits or with a risk of harm outweighing patient-relevant benefits and/or (b) are when a comparable health outcome could be achieved in lower resource settings using the resources available in that place/health care system, and/or (c) violate a patient's/informal caregiver's preference or an agreed care plan." Discussion and Implications: Consensus on a definition for "avoidable care transitions" was reached by a multidisciplinary and international panel of experts comprising researchers and providers. The resulting definition consists of 3 distinct dimensions relating to the balance of benefit and harm to a patient, resource consumption, and a patient's or informal caregiver's preferences. The new definition might enhance the common understanding of avoidable care transitions and is now ready for application in research and quality and safety management in health care.
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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 | 0.192 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.004 |
| 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".