Assessment tools addressing avoidable care transitions in older adults: a systematic literature review
Bibliographic record
Abstract
PURPOSE: The phenomenon of avoidable care transitions has received increasing attention over the last decades due to its frequency and associated burden for the patients and the healthcare system. A number of assessment tools to identify avoidable transitions have been designed and implemented. The selection of the most appropriate tool appears to be challenging and time-consuming. This systematic review aimed to identify and comprehensively describe the assessment tools that can support stakeholders´ care transition decisions on older adults. METHODS: This study was conducted as part of the TRANS-SENIOR research network. A systematic search was conducted in MEDLINE via PubMed, CINAHL, and CENTRAL. No restrictions regarding publication date and language were applied. RESULTS: The search in three electronic databases revealed 1266 references and screening for eligibility resulted in 58 articles for inclusion. A total of 48 assessment tools were identified covering different concepts, judgement processes, and transition destinations. We found variation in the comprehensiveness of the tools with regard to dimensions used in the judgement process. CONCLUSION: All tools are not comprehensive with respect to the dimensions covered, as they address only one or a few perspectives. Although assessment tools can be useful in clinical practice, it is worth it to bear in mind that they are meant to support decision-making and supplement the care professional´s judgement, instead of replacing it. Our review might guide clinicians and researchers in choosing the right tool for identification of avoidable care transitions, and thus support informed decision-making.
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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 | 0.016 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".