EFFECTIVENESS OF INTERVENTIONS TO REDUCE ACUTE CARE TRANSFERS FROM NURSING HOMES: A META-ANALYSIS
Bibliographic record
Abstract
Transferring patients from the nursing home (NH) to the acute care setting is associated with increased mortality and morbidity. To date, several types of interventions seeking to reduce potentially avoidable hospital transfers have been proposed, yet there is a lack of systematic evidence regarding their effectiveness. In response to this knowledge gap, we conducted a systematic review to assess the effectiveness of interventions aimed at reducing emergency department (ED) transfers and hospital admissions (HA) from NH. MEDLINE, CINAHL, EMBASE, Social Work Abstracts, and other relevant scientific literature databases were searched from inception until July 2016 for primary studies using quantitative and mixed methods. Forward and backward citation tracking techniques and a grey literature review were also conducted. A random-effects model meta-analysis was conducted for each outcome (rate ratio reduction in ED and HA rates per 100 resident-days). In total, 17 unique studies provided 26 usable samples pertaining to ED and/or HA rates. For both outcome types there was a significant reduction in transfer rates across studies (RR=0.82; 95%CI=0.68–0.99; overall effect Z=2.07, p=0.04 for ED and RR=0.73; 95%CI=0.65–0.83; overall effect Z=4.76, p<0.00001 for HA) despite high statistical heterogeneity (I2>75% in both cases). Although studies targeted a variety of transfer-related factors, interventions appeared more effective in reducing HA than ED transfers. This suggests that HA could be a better target for these interventions. Importantly, our systematic review has also revealed a lack of consistency across studies regarding outcome operationalization, measurement and data reporting.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.062 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".