Identifying Effective Nurse‐Led Care Transition Interventions for Older Adults With Complex Needs Using a Structured Expert Panel
Bibliographic record
Abstract
BACKGROUND: Nursing plays a central role in facilitating care transitions for complex older adults, yet there is no consensus of the components of nurse-led care transitions interventions to facilitate high quality care transitions among complex older adults. A structured expert panel was established with the purpose of identifying effective nurse-led care transition interventions. METHODS: A modified Delphi consensus technique based on the RAND method was employed. Panelists (n = 23) were asked to individually rate a series of statements derived from a realist synthesis of the literature for relevance, feasibility and likely impact. Statements receiving an aggregate score of ≥75% (7/9) were reviewed and revised at a face-to-face consensus meeting. A second round of rating following the same process as round one was used, followed by a final ranking of the statements. RESULTS: The five highest ranked intervention components and contextual factors were: (a) educating and coaching patients, their family members and caregivers about self-management skills; (b) ensuring patients, their family members and caregivers are aware of follow-up medical appointments and postdischarge care plan; (c) using standardized documentation tools and comprehensive communication strategies during care transitions; (d) optimizing nurses' roles and scopes of practice across the care transitions spectrum; and (e) having strong leadership, strategic alignment and accountability structures in organizations to enable quality care transitions for the complex older person population. LINKING EVIDENCE TO ACTION: Key insights on optimizing the nurses' roles and scope of practice during care transitions included having nurses provide "warm hand-offs" and serve as the "go-to person." The panel also identified current challenges to optimizing the nurses' roles and scope of practice across care transition points. Future research is required to determine effective nurse-led intervention components and in which context do they work or do not.
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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 | 0.105 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| 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".