Discharge Planning From Hospital to Home for Elderly Patients: A Meta-Analysis
Bibliographic record
Abstract
In the present healthcare environment, budget cuts, staff shortages, and resource limitations are grave concerns. The elderly in particular consume a considerable proportion of hospital resources. Thus, the discharge planner's role, particularly with respect to elderly patients, is extremely important. In this systematic review recent (within the last 10 years) randomized, controlled or quasi-experimental trials of discharge planning (DP) from hospital to home of patients age 65 years or older were examined. The most important finding was the paucity of investigations by social work professionals. A second important finding was the lack of appropriate reporting of methods and results. Where data were provided, an effect size was computed for statistically significant results (overall mean d = 0.51, SD 0.35). Large effects were noted for patient satisfaction, while moderate effects were evident for patients' quality of life and readmission rates. The integration and evaluation of current knowledge in this field may inform further research and may lead to the advancement of clinical practice and new policy development, with the ultimate goal of improving the quality of patient care and the quality of patient outcomes. The implications for social work clinicians and researchers are discussed.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.031 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".