Enhancing Our Understanding of Transitional Care Programs
Bibliographic record
Abstract
Abstract Many hospitalized older adults experience delayed discharge. Transitional care programs (TCPs) provide short-term care to these patients to prepare them for transfer to nursing homes or back to the community. There are knowledge gaps related to the processes and outcomes of TCPs. We conducted a scoping review following Arksey & O’Malley’s framework to identify the: 1) characteristics of older patients served by TCPs, 2) services provided within TCPs, and 3) outcomes used to evaluate TCPs. We searched bibliographic databases and grey literature. We included papers and reports involving community-dwelling older adults aged ≥ 65 years and examined the processes and/or outcomes of TCPs. The search retrieved 4828 references; 38 studies and 2 reports met the inclusion criteria. Most studies were conducted in Europe (n=19) and America (n=13). Patients admitted to TCPs were 59-86 years old, had 2-10 chronic conditions, 26-74% lived alone, the majority were functionally dependent and had mild cognitive impairment. Most TCPs were staffed by nurses, physiotherapists, occupational therapists, social workers and physicians, and support staff. The TCPs provided 5 major types of services: assessment, care planning, treatment, evaluation/care monitoring and discharge planning. The outcomes most frequently assessed were discharge destination, mortality, hospital readmission, length of stay, cost and functional status. TCPs that reported significant improvement in older adults’ functions (which was the main goal of the TCPs) included multiple services delivered by multidisciplinary teams. There is a wide variation in the operationalization of TCPs within and between countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| 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".