Crossing boundaries: Establishing a framework for researching quality and safety in care transitions
Bibliographic record
Abstract
BACKGROUND: Despite the breadth and diversity of research and policies on care transitions, research studies often report similar components that affect the quality and safety of care, including communication across professional groups and care settings, transfer of information, coordination of resources or training of healthcare personnel. In this article, we aim to deepen our understanding of care transitions by proposing a heuristic research framework that takes into account the components and factors influencing the quality and safety of care transitions in diverse settings. METHODOLOGY: Using a pragmatic qualitative narrative meta-synthesis of empirically grounded research studies (N = 13) involving 31 researchers from seven countries (Australia, Canada, Denmark, Germany, the Netherlands, Norway and the UK), we conducted a thematic analysis to identify the components analysed in the included studies. We then used these components to create a framework for researching care transitions. RESULTS: Our narrative synthesis found that the quality and safety of care transitions are influenced by a range of patient-centred, communicative, collaborative, cultural, competency-based, accountability-based and spatial components. These components are encompassed within a broader set of dimensions that require careful consideration: (1) the conceptualising of the care transition notion, (2) the methodology for researching care transitions, (3) the role of patients and carers in care transitions, (4) the complexity surrounding care transitions, (5) the boundaries intertwined in care transitions and (6) care transition improvement interventions. These six dimensions constitute an analytical framework for planning and conducting research on care transitions in diverse settings. CONCLUSION: The proposed six-dimensional framework for researching quality and safety in care transitions offers a roadmap for future practice and policy interventions and provides a starting point for planning and designing future research.
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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 | 0.268 | 0.194 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.033 | 0.025 |
| Science and technology studies | 0.020 | 0.116 |
| Scholarly communication | 0.036 | 0.054 |
| Open science | 0.012 | 0.025 |
| Research integrity | 0.014 | 0.015 |
| 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".